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/1

Discussion Files changed

+49

-1

Files changed (1) hide show

config.json +49 -1

config.json CHANGED Viewed

	@@ -1 +1,49 @@
1	- {"vocab_size": 32000, "max_position_embeddings": 8192, "hidden_size": 4096, "intermediate_size": 14336, "num_hidden_layers": 32, "num_attention_heads": 32, "sliding_window": 4096, "num_key_value_heads": 8, "hidden_act": "silu", "initializer_range": 0.02, "rms_norm_eps": 1e-05, "use_cache": true, "rope_theta": 10000.0, "attention_dropout": 0.0, "return_dict": true, "output_hidden_states": false, "output_attentions": false, "torchscript": false, "torch_dtype": null, "use_bfloat16": false, "tf_legacy_loss": false, "pruned_heads": {}, "tie_word_embeddings": false, "chunk_size_feed_forward": 0, "is_encoder_decoder": false, "is_decoder": false, "cross_attention_hidden_size": null, "add_cross_attention": false, "tie_encoder_decoder": false, "max_length": 20, "min_length": 0, "do_sample": false, "early_stopping": false, "num_beams": 1, "num_beam_groups": 1, "diversity_penalty": 0.0, "temperature": 1.0, "top_k": 50, "top_p": 1.0, "typical_p": 1.0, "repetition_penalty": 1.0, "length_penalty": 1.0, "no_repeat_ngram_size": 0, "encoder_no_repeat_ngram_size": 0, "bad_words_ids": null, "num_return_sequences": 1, "output_scores": false, "return_dict_in_generate": false, "forced_bos_token_id": null, "forced_eos_token_id": null, "remove_invalid_values": false, "exponential_decay_length_penalty": null, "suppress_tokens": null, "begin_suppress_tokens": [1, 2], "architectures": ["MistralForCausalLM"], "finetuning_task": null, "id2label": {"0": "LABEL_0", "1": "LABEL_1"}, "label2id": {"LABEL_0": 0, "LABEL_1": 1}, "tokenizer_class": null, "prefix": null, "bos_token_id": 1, "pad_token_id": 2, "eos_token_id": 2, "sep_token_id": null, "decoder_start_token_id": 1, "task_specific_params": null, "problem_type": null, "_name_or_path": "", "transformers_version": "4.41.1", "model_type": "mistral"}

+{
+  "architectures": ["MistralForCausalLM"],
+  "model_type": "mistral",
+  "dtype": "bfloat16",
+  "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
+  }
+}