Instructions to use GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt with Transformers:

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

pipe = pipeline("text-generation", model="GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt")
model = AutoModelForMultimodalLM.from_pretrained("GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt

SGLang

How to use GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt 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 "GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt" \
    --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": "GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt",
		"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 "GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt" \
        --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": "GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt with Docker Model Runner:
```
docker model run hf.co/GritLM/gen_m7_sq2048_tulu2_ep1_zephfmt
```

gen_m7_sq2048_tulu2_ep1_zephfmt / config.json

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	{
	"_name_or_path": "mistralai/Mistral-7B-v0.1",
	"architectures": [
	"MistralForCausalLM"
	],
	"bos_token_id": 1,
	"eos_token_id": 2,
	"hidden_act": "silu",
	"hidden_size": 4096,
	"id2label": {
	"0": "LABEL_0"
	},
	"initializer_range": 0.02,
	"intermediate_size": 14336,
	"label2id": {
	"LABEL_0": 0
	},
	"max_position_embeddings": 32768,
	"model_type": "mistral",
	"num_attention_heads": 32,
	"num_hidden_layers": 32,
	"num_key_value_heads": 8,
	"rms_norm_eps": 1e-05,
	"rope_theta": 10000.0,
	"sliding_window": 4096,
	"tie_word_embeddings": false,
	"torch_dtype": "bfloat16",
	"transformers_version": "4.36.0.dev0",
	"use_cache": true,
	"vocab_size": 32000
	}