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modification on llamacpp readme after Ipex-llm latest update (#11971)

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* update on readme after ipex-llm update

* update on readme after ipex-llm update

* rebase & delete redundancy

* revise

* add numbers for troubleshooting
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8
docs/mddocs/Quickstart/llama3_llamacpp_ollama_quickstart.md
View file

@ -80,7 +80,7 @@ Under your current directory, exceuting below command to do inference with Llama
- For **Linux users**:
```bash
./main -m <model_dir>/Meta-Llama-3-8B-Instruct-Q4_K_M.gguf -n 32 --prompt "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun doing something" -t 8 -e -ngl 33 --color --no-mmap
./llama-cli -m <model_dir>/Meta-Llama-3-8B-Instruct-Q4_K_M.gguf -n 32 --prompt "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun doing something" -c 1024 -t 8 -e -ngl 33 --color --no-mmap
```
- For **Windows users**:
@ -88,7 +88,7 @@ Under your current directory, exceuting below command to do inference with Llama
Please run the following command in Miniforge Prompt.
```cmd
main -m <model_dir>/Meta-Llama-3-8B-Instruct-Q4_K_M.gguf -n 32 --prompt "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun doing something" -e -ngl 33 --color --no-mmap
llama-cli -m <model_dir>/Meta-Llama-3-8B-Instruct-Q4_K_M.gguf -n 32 --prompt "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun doing something" -c 1024 -e -ngl 33 --color --no-mmap
```
Under your current directory, you can also execute below command to have interactive chat with Llama3:
@ -96,7 +96,7 @@ Under your current directory, you can also execute below command to have interac
- For **Linux users**:
```bash
./main -ngl 33 --interactive-first --color -e --in-prefix '<|start_header_id|>user<|end_header_id|>\n\n' --in-suffix '<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n' -r '<|eot_id|>' -m <model_dir>/Meta-Llama-3-8B-Instruct-Q4_K_M.gguf
./llama-cli -ngl 33 --interactive-first --color -e --in-prefix '<|start_header_id|>user<|end_header_id|>\n\n' --in-suffix '<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n' -r '<|eot_id|>' -m <model_dir>/Meta-Llama-3-8B-Instruct-Q4_K_M.gguf -c 1024
```
- For **Windows users**:
@ -104,7 +104,7 @@ Under your current directory, you can also execute below command to have interac
Please run the following command in Miniforge Prompt.
```cmd
main -ngl 33 --interactive-first --color -e --in-prefix "<|start_header_id|>user<|end_header_id|>\n\n" --in-suffix "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" -r "<|eot_id|>" -m <model_dir>/Meta-Llama-3-8B-Instruct-Q4_K_M.gguf
llama-cli -ngl 33 --interactive-first --color -e --in-prefix "<|start_header_id|>user<|end_header_id|>\n\n" --in-suffix "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" -r "<|eot_id|>" -m <model_dir>/Meta-Llama-3-8B-Instruct-Q4_K_M.gguf -c 1024
```
Below is a sample output on Intel Arc GPU:

136
docs/mddocs/Quickstart/llama_cpp_quickstart.md
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@ -1,6 +1,6 @@
# Run llama.cpp with IPEX-LLM on Intel GPU
[ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp) prvoides fast LLM inference in in pure C++ across a variety of hardware; you can now use the C++ interface of [`ipex-llm`](https://github.com/intel-analytics/ipex-llm) as an accelerated backend for `llama.cpp` running on Intel **GPU** *(e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max)*.
[ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp) prvoides fast LLM inference in pure C++ across a variety of hardware; you can now use the C++ interface of [`ipex-llm`](https://github.com/intel-analytics/ipex-llm) as an accelerated backend for `llama.cpp` running on Intel **GPU** *(e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max)*.
See the demo of running LLaMA2-7B on Intel Arc GPU below.
@ -149,7 +149,7 @@ Before running, you should download or copy community GGUF model to your current
- For **Linux users**:
```bash
./main -m mistral-7b-instruct-v0.1.Q4_K_M.gguf -n 32 --prompt "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun" -t 8 -e -ngl 99 --color
./llama-cli -m mistral-7b-instruct-v0.1.Q4_K_M.gguf -n 32 --prompt "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun" -c 1024 -t 8 -e -ngl 99 --color
```
> **Note**:
@ -161,7 +161,7 @@ Before running, you should download or copy community GGUF model to your current
Please run the following command in Miniforge Prompt.
```cmd
main -m mistral-7b-instruct-v0.1.Q4_K_M.gguf -n 32 --prompt "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun" -t 8 -e -ngl 99 --color
llama-cli -m mistral-7b-instruct-v0.1.Q4_K_M.gguf -n 32 --prompt "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun" -c 1024 -t 8 -e -ngl 99 --color
```
> **Note**:
@ -171,24 +171,10 @@ Before running, you should download or copy community GGUF model to your current
#### Sample Output
```
Log start
main: build = 1 (38bcbd4)
main: built with Intel(R) oneAPI DPC++/C++ Compiler 2024.0.0 (2024.0.0.20231017) for x86_64-unknown-linux-gnu
main: seed = 1710359960
ggml_init_sycl: GGML_SYCL_DEBUG: 0
ggml_init_sycl: GGML_SYCL_F16: no
found 8 SYCL devices:
|ID| Name |compute capability|Max compute units|Max work group|Max sub group|Global mem size|
|--|---------------------------------------------|------------------|-----------------|--------------|-------------|---------------|
| 0| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136|
| 1| Intel(R) FPGA Emulation Device| 1.2| 32| 67108864| 64| 67181625344|
| 2| 13th Gen Intel(R) Core(TM) i9-13900K| 3.0| 32| 8192| 64| 67181625344|
| 3| Intel(R) Arc(TM) A770 Graphics| 3.0| 512| 1024| 32| 16225243136|
| 4| Intel(R) Arc(TM) A770 Graphics| 3.0| 512| 1024| 32| 16225243136|
| 5| Intel(R) UHD Graphics 770| 3.0| 32| 512| 32| 53745299456|
| 6| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136|
| 7| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53745299456|
detect 2 SYCL GPUs: [0,6] with Max compute units:512
llama_model_loader: loaded meta data with 20 key-value pairs and 291 tensors from ~/mistral-7b-instruct-v0.1.Q4_K_M.gguf (version GGUF V2)
main: build = 1 (6f4ec98)
main: built with MSVC 19.39.33519.0 for
main: seed = 1724921424
llama_model_loader: loaded meta data with 20 key-value pairs and 291 tensors from D:\gguf-models\mistral-7b-instruct-v0.1.Q4_K_M.gguf (version GGUF V2)
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = mistralai_mistral-7b-instruct-v0.1
@ -213,18 +199,21 @@ llama_model_loader: - kv 19: general.quantization_version u32
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q4_K: 193 tensors
llama_model_loader: - type q6_K: 33 tensors
llm_load_vocab: special tokens definition check successful ( 259/32000 ).
llm_load_vocab: special tokens cache size = 3
llm_load_vocab: token to piece cache size = 0.1637 MB
llm_load_print_meta: format = GGUF V2
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 4
@ -234,112 +223,135 @@ llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 14336
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attm = 1
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 32768
llm_load_print_meta: n_ctx_orig_yarn = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = Q4_K - Medium
llm_load_print_meta: model params = 7.24 B
llm_load_print_meta: model size = 4.07 GiB (4.83 BPW)
llm_load_print_meta: model size = 4.07 GiB (4.83 BPW)
llm_load_print_meta: general.name = mistralai_mistral-7b-instruct-v0.1
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory
get_memory_info: [warning] ext_intel_free_memory is not supported (export/set ZES_ENABLE_SYSMAN=1 to support), use total memory as free memory
llm_load_tensors: ggml ctx size = 0.33 MiB
llm_load_print_meta: max token length = 48
ggml_sycl_init: GGML_SYCL_FORCE_MMQ: no
ggml_sycl_init: SYCL_USE_XMX: yes
ggml_sycl_init: found 1 SYCL devices:
llm_load_tensors: ggml ctx size = 0.27 MiB
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors: SYCL0 buffer size = 2113.28 MiB
llm_load_tensors: SYCL6 buffer size = 1981.77 MiB
llm_load_tensors: SYCL_Host buffer size = 70.31 MiB
...............................................................................................
llm_load_tensors: SYCL0 buffer size = 4095.05 MiB
llm_load_tensors: CPU buffer size = 70.31 MiB
..............................................................................................
llama_new_context_with_model: n_ctx = 512
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: SYCL0 KV buffer size = 34.00 MiB
llama_kv_cache_init: SYCL6 KV buffer size = 30.00 MiB
[SYCL] call ggml_check_sycl
ggml_check_sycl: GGML_SYCL_DEBUG: 0
ggml_check_sycl: GGML_SYCL_F16: no
found 1 SYCL devices:
| | | | |Max | |Max |Global | |
| | | | |compute|Max work|sub |mem | |
|ID| Device Type| Name|Version|units |group |group|size | Driver version|
|--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|
| 0| [level_zero:gpu:0]| Intel Arc Graphics| 1.3| 112| 1024| 32| 13578M| 1.3.27504|
llama_kv_cache_init: SYCL0 KV buffer size = 64.00 MiB
llama_new_context_with_model: KV self size = 64.00 MiB, K (f16): 32.00 MiB, V (f16): 32.00 MiB
llama_new_context_with_model: SYCL_Host input buffer size = 10.01 MiB
llama_new_context_with_model: SYCL0 compute buffer size = 73.00 MiB
llama_new_context_with_model: SYCL6 compute buffer size = 73.00 MiB
llama_new_context_with_model: SYCL_Host compute buffer size = 8.00 MiB
llama_new_context_with_model: graph splits (measure): 3
system_info: n_threads = 8 / 32 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 |
sampling:
repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000
llama_new_context_with_model: SYCL_Host output buffer size = 0.12 MiB
llama_new_context_with_model: SYCL0 compute buffer size = 81.00 MiB
llama_new_context_with_model: SYCL_Host compute buffer size = 9.01 MiB
llama_new_context_with_model: graph nodes = 902
llama_new_context_with_model: graph splits = 2
system_info: n_threads = 8 / 18 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 |
sampling:
repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampling order:
CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temperature
generate: n_ctx = 512, n_batch = 512, n_predict = 32, n_keep = 1
Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun exploring the world around her. Her parents were kind and let her do what she wanted, as long as she stayed safe.
One day, the little
llama_print_timings: load time = 10096.78 ms
sampling order:
CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temperature
generate: n_ctx = 512, n_batch = 2048, n_predict = 32, n_keep = 1
Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun exploring the world. She lived in a small village where there weren't many opportunities for adventures, but that didn't stop her. She would often read
llama_print_timings: load time = xxxx ms
llama_print_timings: sample time = x.xx ms / 32 runs ( xx.xx ms per token, xx.xx tokens per second)
llama_print_timings: prompt eval time = xx.xx ms / 31 tokens ( xx.xx ms per token, xx.xx tokens per second)
llama_print_timings: eval time = xx.xx ms / 31 runs ( xx.xx ms per token, xx.xx tokens per second)
llama_print_timings: total time = xx.xx ms / 62 tokens
Log end
```
### Troubleshooting
#### Unable to run the initialization script
#### 1. Unable to run the initialization script
If you are unable to run `init-llama-cpp.bat`, please make sure you have installed `ipex-llm[cpp]` in your conda environment. If you have installed it, please check if you have activated the correct conda environment. Also, if you are using Windows, please make sure you have run the script with administrator privilege in prompt terminal.
#### `DeviceList is empty. -30 (PI_ERROR_INVALID_VALUE)` error
#### 2. `DeviceList is empty. -30 (PI_ERROR_INVALID_VALUE)` error
On Linux, this error happens when devices starting with `[ext_oneapi_level_zero]` are not found. Please make sure you have installed level-zero, and have sourced `/opt/intel/oneapi/setvars.sh` before running the command.
#### `Prompt is too long` error
#### 3. `Prompt is too long` error
If you encounter `main: prompt is too long (xxx tokens, max xxx)`, please increase the `-c` parameter to set a larger size of context.
#### `gemm: cannot allocate memory on host` error / `could not create an engine` error
#### 4. `gemm: cannot allocate memory on host` error / `could not create an engine` error
If you meet `oneapi::mkl::oneapi::mkl::blas::gemm: cannot allocate memory on host` error, or `could not create an engine` on Linux, this is probably caused by pip installed OneAPI dependencies. You should prevent installing like `pip install dpcpp-cpp-rt==2024.0.2 mkl-dpcpp==2024.0.0 onednn==2024.0.0`, and instead use `apt` to install on Linux. Please refer to [this guide](./install_linux_gpu.md) for more details.
#### Fail to quantize model
#### 5. Fail to quantize model
If you encounter `main: failed to quantize model from xxx`, please make sure you have created related output directory.
#### Program hang during model loading
#### 6. Program hang during model loading
If your program hang after `llm_load_tensors: SYCL_Host buffer size = xx.xx MiB`, you can add `--no-mmap` in your command.
#### How to set `-ngl` parameter
#### 7. How to set `-ngl` parameter
`-ngl` means the number of layers to store in VRAM. If your VRAM is enough, we recommend putting all layers on GPU, you can just set `-ngl` to a large number like 999 to achieve this goal.
If `-ngl` is set to 0, it means that the entire model will run on CPU. If `-ngl` is set to greater than 0 and less than model layers, then it's mixed GPU + CPU scenario.
#### How to specificy GPU
#### 8. How to specificy GPU
If your machine has multi GPUs, `llama.cpp` will default use all GPUs which may slow down your inference for model which can run on single GPU. You can add `-sm none` in your command to use one GPU only.
Also, you can use `ONEAPI_DEVICE_SELECTOR=level_zero:[gpu_id]` to select device before excuting your command, more details can refer to [here](../Overview/KeyFeatures/multi_gpus_selection.md#2-oneapi-device-selector).
#### Program crash with Chinese prompt
#### 9. Program crash with Chinese prompt
If you run the llama.cpp program on Windows and find that your program crashes or outputs abnormally when accepting Chinese prompts, you can open `Region->Administrative->Change System locale..`, check `Beta: Use Unicode UTF-8 for worldwide language support` option and then restart your computer.
For detailed instructions on how to do this, see [this issue](https://github.com/intel-analytics/ipex-llm/issues/10989#issuecomment-2105600469).
#### sycl7.dll not found error
#### 10. sycl7.dll not found error
If you meet `System Error: sycl7.dll not found` on Windows or you meet similar error on Linux, please check:
1. if you have installed conda and if you are in the right conda environment which has pip installed oneapi dependencies on Windows
2. if you have executed `source /opt/intel/oneapi/setvars.sh` on Linux
#### Check driver first when you meet garbage output
#### 11. Check driver first when you meet garbage output
If you meet garbage output, please check if your GPU driver version is >= [31.0.101.5522](https://www.intel.cn/content/www/cn/zh/download/785597/823163/intel-arc-iris-xe-graphics-windows.html). If not, please follow the instructions in [this section](./install_linux_gpu.md#install-gpu-driver) to update your GPU driver.
#### Why my program can't find sycl device
If you meet `GGML_ASSERT: C:/Users/Administrator/actions-runner/cpp-release/_work/llm.cpp/llm.cpp/llama-cpp-bigdl/ggml-sycl.cpp:18283: main_gpu_id<g_all_sycl_device_count` error or similar error, and you find nothing is output when using `ls-sycl-device`, this is because llama.cpp cannot find the sycl device. On some laptops, the installation of the ARC driver may lead to a forced installation of `OpenCL, OpenGL, and Vulkan Compatibility Pack` by Microsoft, which inadvertently blocks the system from locating sycl devices. This issue can be resolved by manually uninstalling it in Microsoft store.
#### 12. Why my program can't find sycl device
If you meet `GGML_ASSERT: C:/Users/Administrator/actions-runner/cpp-release/_work/llm.cpp/llm.cpp/llama-cpp-bigdl/ggml-sycl.cpp:18283: main_gpu_id<g_all_sycl_device_count` error or similar error, and you find nothing is output when using `ls-sycl-device`, this is because llama.cpp cannot find the sycl device. On some laptops, the installation of the ARC driver may lead to a forced installation of `OpenCL, OpenGL, and Vulkan Compatibility Pack` by Microsoft, which inadvertently blocks the system from locating sycl devices. This issue can be resolved by manually uninstalling it in Microsoft store.
#### 13. Core dump when having both integrated and dedicated graphics
If you have both integrated and dedicated graphics displayed in your llama.cpp's device log and don't specify which device to use, it will cause a core dump. In such case, you may need to specify `export ONEAPI_DEVICE_SELECTOR=level_zero:0` before running `llama-cli`.
#### 14. `Native API failed` error
On latest version of `ipex-llm`, you might come across `native API failed` error with certain models without the `-c` parameter. Simply adding `-c xx` would resolve this problem.

12
docs/mddocs/Quickstart/ollama_quickstart.md
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@ -191,24 +191,24 @@ An example process of interacting with model with `ollama run example` looks lik
</a>
### Troubleshooting
#### Unable to run the initialization script
#### 1. Unable to run the initialization script
If you are unable to run `init-ollama.bat`, please make sure you have installed `ipex-llm[cpp]` in your conda environment. If you have installed it, please check if you have activated the correct conda environment. Also, if you are using Windows, please make sure you have run the script with administrator privilege in prompt terminal.
#### Why model is always loaded again after several minutes
#### 2. Why model is always loaded again after several minutes
Ollama will unload model from gpu memory in every 5 minutes as default. For latest version of ollama, you could set `OLLAMA_KEEP_ALIVE=-1` to keep the model loaded in memory. Reference issue: https://github.com/intel-analytics/ipex-llm/issues/11608
#### `exit status 0xc0000135` error when executing `ollama serve`
#### 3. `exit status 0xc0000135` error when executing `ollama serve`
When executing `ollama serve`, if you meet `llama runner process has terminated: exit status 0xc0000135` on Windows or you meet `ollama_llama_server: error while loading shared libraries: libmkl_core.so.2: cannot open shared object file` on Linux, this is most likely caused by the lack of sycl dependency. Please check:
1. if you have installed conda and if you are in the right conda environment which has pip installed oneapi dependencies on Windows
2. if you have executed `source /opt/intel/oneapi/setvars.sh` on Linux
#### Program hang during initial model loading stage
#### 4. Program hang during initial model loading stage
When launching `ollama serve` for the first time on Windows, it may get stuck during the model loading phase. If you notice that the program is hanging for a long time during the first run, you can manually input a space or other characters on the server side to ensure the program is running.
#### How to distinguish the community version of Ollama from the ipex-llm version of Ollama
#### 5. How to distinguish the community version of Ollama from the ipex-llm version of Ollama
In the server log of community version of Ollama, you may see `source=payload_common.go:139 msg="Dynamic LLM libraries [rocm_v60000 cpu_avx2 cuda_v11 cpu cpu_avx]"`.
But in the server log of ipex-llm version of Ollama, you should only see `source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2]"`.
#### Ollama hang when multiple different questions is asked or context is long
#### 6. Ollama hang when multiple different questions is asked or context is long
If you find ollama hang when multiple different questions is asked or context is long, and you see `update_slots : failed to free spaces in the KV cache` in the server log, this could be because that sometimes the LLM context is larger than the default `n_ctx` value, you may increase the `n_ctx` and try it again.