Instructions to use bertin-project/Gromenauer-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bertin-project/Gromenauer-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="bertin-project/Gromenauer-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bertin-project/Gromenauer-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("bertin-project/Gromenauer-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bertin-project/Gromenauer-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bertin-project/Gromenauer-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bertin-project/Gromenauer-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bertin-project/Gromenauer-7B-Instruct

SGLang

How to use bertin-project/Gromenauer-7B-Instruct 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 "bertin-project/Gromenauer-7B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bertin-project/Gromenauer-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "bertin-project/Gromenauer-7B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bertin-project/Gromenauer-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bertin-project/Gromenauer-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/bertin-project/Gromenauer-7B-Instruct
```

Update config.json

by alvp - opened Mar 31

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+21

-37

Files changed (1) hide show

config.json +21 -37

config.json CHANGED Viewed

@@ -5,45 +5,29 @@
   "tie_word_embeddings": false,
   "transformers_version": "5.4.0",
-  "text_config": {
-    "model_type": "mistral",
-    "vocab_size": 32000,
-    "hidden_size": 4096,
-    "intermediate_size": 14336,
-    "num_hidden_layers": 32,
-    "num_attention_heads": 32,
-    "num_key_value_heads": 8,
-    "head_dim": 128,
-    "hidden_act": "silu",
-    "max_position_embeddings": 8192,
-    "sliding_window": 4096,
-    "rms_norm_eps": 1e-05,
-    "initializer_range": 0.02,
-    "attention_bias": false,
-    "attention_dropout": 0.0,
-    "mlp_bias": false,
-    "rope_parameters": {
-      "rope_theta": 10000.0,
-      "rope_type": "default"
-    },
-    "use_cache": true,
-    "tie_word_embeddings": false,
-    "pretraining_tp": 1,
-    "bos_token_id": 1,
-    "eos_token_id": 2,
-    "pad_token_id": 2,
-    "do_sample": true,
-    "temperature": 0.7,
-    "top_k": 0,
-    "top_p": 0.9,
-    "repetition_penalty": 1.1,
-    "max_length": 4096,
-    "min_length": 0
-  }
 }

   "tie_word_embeddings": false,
   "transformers_version": "5.4.0",
+  "vocab_size": 32000,
+  "hidden_size": 4096,
+  "intermediate_size": 14336,
+  "num_hidden_layers": 32,
+  "num_attention_heads": 32,
+  "num_key_value_heads": 8,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "max_position_embeddings": 8192,
+  "sliding_window": 4096,
+  "rms_norm_eps": 1e-05,
+  "initializer_range": 0.02,
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "mlp_bias": false,
+  "rope_theta": 10000.0,
+  "use_cache": true,
+  "pretraining_tp": 1,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2
 }