Instructions to use DanielClough/Candle_Mistral-7B-Instruct-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DanielClough/Candle_Mistral-7B-Instruct-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="DanielClough/Candle_Mistral-7B-Instruct-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DanielClough/Candle_Mistral-7B-Instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("DanielClough/Candle_Mistral-7B-Instruct-v0.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DanielClough/Candle_Mistral-7B-Instruct-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DanielClough/Candle_Mistral-7B-Instruct-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DanielClough/Candle_Mistral-7B-Instruct-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DanielClough/Candle_Mistral-7B-Instruct-v0.1

SGLang

How to use DanielClough/Candle_Mistral-7B-Instruct-v0.1 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 "DanielClough/Candle_Mistral-7B-Instruct-v0.1" \
    --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": "DanielClough/Candle_Mistral-7B-Instruct-v0.1",
		"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 "DanielClough/Candle_Mistral-7B-Instruct-v0.1" \
        --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": "DanielClough/Candle_Mistral-7B-Instruct-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DanielClough/Candle_Mistral-7B-Instruct-v0.1 with Docker Model Runner:
```
docker model run hf.co/DanielClough/Candle_Mistral-7B-Instruct-v0.1
```

Ubuntu commited on Dec 11, 2023

Commit

a7395e4

1 Parent(s): d1625c3

init

Browse files

Files changed (7) hide show

.gitattributes +1 -0
Candle_Mistral-7B-Instruct-v0.1_q6k.gguf +3 -0
README.md +1 -0
convert.py +9 -0
pytorch_model-00001-of-00002.safetensors +3 -0
pytorch_model-00002-of-00002.safetensors +3 -0
tokenizer.json +0 -0

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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
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README.md CHANGED Viewed

@@ -1,3 +1,4 @@
 ---
 license: apache-2.0
 ---

 ---
 license: apache-2.0
 ---
+Refer to the main [model card](https://huggingface.co/mistralai/Mistral-Instruct-v0.1), this repo holds a simple safetensor conversion of the weights.

convert.py ADDED Viewed

	@@ -0,0 +1,9 @@

+import torch
+from safetensors import safe_open
+from safetensors.torch import save_file
+for d in [1, 2]:
+    print(d)
+    model = torch.load(f"pytorch_model-0000{d}-of-00002.bin")
+    print("loaded", d, len(model))
+    save_file(model, f"pytorch_model-0000{d}-of-00002.safetensors")

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