Instructions to use unsloth/Mistral-Nemo-Base-2407 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/Mistral-Nemo-Base-2407 with Transformers:

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

pipe = pipeline("text-generation", model="unsloth/Mistral-Nemo-Base-2407")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("unsloth/Mistral-Nemo-Base-2407")
model = AutoModelForCausalLM.from_pretrained("unsloth/Mistral-Nemo-Base-2407", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use unsloth/Mistral-Nemo-Base-2407 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/Mistral-Nemo-Base-2407"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/Mistral-Nemo-Base-2407",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/unsloth/Mistral-Nemo-Base-2407

SGLang

How to use unsloth/Mistral-Nemo-Base-2407 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 "unsloth/Mistral-Nemo-Base-2407" \
    --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": "unsloth/Mistral-Nemo-Base-2407",
		"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 "unsloth/Mistral-Nemo-Base-2407" \
        --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": "unsloth/Mistral-Nemo-Base-2407",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use unsloth/Mistral-Nemo-Base-2407 with Docker Model Runner:
```
docker model run hf.co/unsloth/Mistral-Nemo-Base-2407
```

danielhanchen commited on Aug 6, 2024

Commit

766115e

verified ·

1 Parent(s): 091cb3e

Upload config

Browse files

Files changed (1) hide show

config.json +3 -1

config.json CHANGED Viewed

@@ -16,12 +16,14 @@
   "num_attention_heads": 32,
   "num_hidden_layers": 40,
   "num_key_value_heads": 8,
   "rms_norm_eps": 1e-05,
   "rope_theta": 1000000.0,
   "sliding_window": null,
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
-  "transformers_version": "4.43.0.dev0",
   "use_cache": true,
   "vocab_size": 131072
 }

   "num_attention_heads": 32,
   "num_hidden_layers": 40,
   "num_key_value_heads": 8,
+  "pad_token_id": 10,
   "rms_norm_eps": 1e-05,
   "rope_theta": 1000000.0,
   "sliding_window": null,
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
+  "transformers_version": "4.44.0",
+  "unsloth_version": "2024.8",
   "use_cache": true,
   "vocab_size": 131072
 }