Instructions to use s3nh/intfloat-e5-mistral-7b-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use s3nh/intfloat-e5-mistral-7b-instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="s3nh/intfloat-e5-mistral-7b-instruct-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("s3nh/intfloat-e5-mistral-7b-instruct-GGUF", dtype="auto")

llama-cpp-python

How to use s3nh/intfloat-e5-mistral-7b-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="s3nh/intfloat-e5-mistral-7b-instruct-GGUF",
	filename="e5-mistral.Q3_K_S.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use s3nh/intfloat-e5-mistral-7b-instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf s3nh/intfloat-e5-mistral-7b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf s3nh/intfloat-e5-mistral-7b-instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf s3nh/intfloat-e5-mistral-7b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf s3nh/intfloat-e5-mistral-7b-instruct-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf s3nh/intfloat-e5-mistral-7b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf s3nh/intfloat-e5-mistral-7b-instruct-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf s3nh/intfloat-e5-mistral-7b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf s3nh/intfloat-e5-mistral-7b-instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/s3nh/intfloat-e5-mistral-7b-instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use s3nh/intfloat-e5-mistral-7b-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "s3nh/intfloat-e5-mistral-7b-instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "s3nh/intfloat-e5-mistral-7b-instruct-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/s3nh/intfloat-e5-mistral-7b-instruct-GGUF:Q4_K_M

SGLang

How to use s3nh/intfloat-e5-mistral-7b-instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "s3nh/intfloat-e5-mistral-7b-instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "s3nh/intfloat-e5-mistral-7b-instruct-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "s3nh/intfloat-e5-mistral-7b-instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "s3nh/intfloat-e5-mistral-7b-instruct-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use s3nh/intfloat-e5-mistral-7b-instruct-GGUF with Ollama:
```
ollama run hf.co/s3nh/intfloat-e5-mistral-7b-instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use s3nh/intfloat-e5-mistral-7b-instruct-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for s3nh/intfloat-e5-mistral-7b-instruct-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for s3nh/intfloat-e5-mistral-7b-instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for s3nh/intfloat-e5-mistral-7b-instruct-GGUF to start chatting

Docker Model Runner
How to use s3nh/intfloat-e5-mistral-7b-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/s3nh/intfloat-e5-mistral-7b-instruct-GGUF:Q4_K_M
```

Lemonade

How to use s3nh/intfloat-e5-mistral-7b-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull s3nh/intfloat-e5-mistral-7b-instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.intfloat-e5-mistral-7b-instruct-GGUF-Q4_K_M

List all available models

lemonade list

can we get a config.json?

by sirus - opened Feb 8, 2024

Discussion

sirus

Feb 8, 2024

llm_load_tensors: ggml ctx size = 0.11 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: CPU buffer size = 7338.65 MiB
...................................................................................................
llama_new_context_with_model: n_ctx = 4000
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 500.00 MiB
llama_new_context_with_model: KV self size = 500.00 MiB, K (f16): 250.00 MiB, V (f16): 250.00 MiB
llama_new_context_with_model: CPU input buffer size = 15.83 MiB
llama_new_context_with_model: CPU compute buffer size = 301.40 MiB
llama_new_context_with_model: graph splits (measure): 1
AVX = 1 | AVX_VNNI = 0 | 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 = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 |
Model metadata: {'tokenizer.ggml.add_eos_token': 'false', 'tokenizer.ggml.padding_token_id': '2', 'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.eos_token_id': '2', 'general.architecture': 'llama', 'llama.rope.freq_base': '10000.000000', 'llama.context_length': '32768', 'general.name': 'cognitivecomputations_dolphin-2.6-mistral-7b-dpo-laser', 'tokenizer.ggml.add_bos_token': 'true', 'llama.embedding_length': '4096', 'llama.feed_forward_length': '14336', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'llama.rope.dimension_count': '128', 'tokenizer.ggml.bos_token_id': '1', 'llama.attention.head_count': '32', 'llama.block_count': '32', 'llama.attention.head_count_kv': '8', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'llama', 'general.file_type': '7'}
Traceback (most recent call last):
File "/usr/lib/python3.11/site-packages/huggingface_hub/utils/_errors.py", line 286, in hf_raise_for_status
response.raise_for_status()
File "/usr/lib/python3.11/site-packages/requests/models.py", line 1021, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/s3nh/intfloat-e5-mistral-7b-instruct-GGUF/resolve/main/config.json

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/usr/lib/python3.11/site-packages/transformers/utils/hub.py", line 389, in cached_file
resolved_file = hf_hub_download(
^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 1238, in hf_hub_download
metadata = get_hf_file_metadata(
^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 1631, in get_hf_file_metadata
r = _request_wrapper(
^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 385, in _request_wrapper
response = _request_wrapper(
^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 409, in _request_wrapper
hf_raise_for_status(response)
File "/usr/lib/python3.11/site-packages/huggingface_hub/utils/_errors.py", line 296, in hf_raise_for_status
raise EntryNotFoundError(message, response) from e
huggingface_hub.utils._errors.EntryNotFoundError: 404 Client Error. (Request ID: Root=1-65c44419-78c9be2554a4a20255f87db9;0af99497-8fda-4ae3-92d6-1dc3a3f8cc5e)

Entry Not Found for url: https://huggingface.co/s3nh/intfloat-e5-mistral-7b-instruct-GGUF/resolve/main/config.json.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "/code/git/ontherag/./myrag.py", line 47, in
embed_model = HuggingFaceEmbedding(model_name="s3nh/intfloat-e5-mistral-7b-instruct-GGUF")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/site-packages/llama_index/embeddings/huggingface.py", line 82, in init
model = AutoModel.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py", line 526, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/site-packages/transformers/models/auto/configuration_auto.py", line 1082, in from_pretrained
config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/site-packages/transformers/configuration_utils.py", line 644, in get_config_dict
config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/site-packages/transformers/configuration_utils.py", line 699, in _get_config_dict
resolved_config_file = cached_file(
^^^^^^^^^^^^
File "/usr/lib/python3.11/site-packages/transformers/utils/hub.py", line 440, in cached_file
raise EnvironmentError(
OSError: s3nh/intfloat-e5-mistral-7b-instruct-GGUF does not appear to have a file named config.json. Checkout 'https://huggingface.co/s3nh/intfloat-e5-mistral-7b-instruct-GGUF/main' for available files.

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