Instructions to use mindfossil/telecom-intelligence-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mindfossil/telecom-intelligence-model with Transformers:

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

pipe = pipeline("text-generation", model="mindfossil/telecom-intelligence-model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mindfossil/telecom-intelligence-model")
model = AutoModelForCausalLM.from_pretrained("mindfossil/telecom-intelligence-model", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mindfossil/telecom-intelligence-model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mindfossil/telecom-intelligence-model

SGLang

How to use mindfossil/telecom-intelligence-model 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 "mindfossil/telecom-intelligence-model" \
    --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": "mindfossil/telecom-intelligence-model",
		"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 "mindfossil/telecom-intelligence-model" \
        --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": "mindfossil/telecom-intelligence-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use mindfossil/telecom-intelligence-model with Docker Model Runner:
```
docker model run hf.co/mindfossil/telecom-intelligence-model
```

mindfossil commited on Jul 23

Commit

0288fc2

verified ·

1 Parent(s): 5b6ffdb

Upload MistralForCausalLM

Browse files

Files changed (1) hide show

config.json +6 -3

config.json CHANGED Viewed

@@ -18,10 +18,13 @@
   "num_key_value_heads": 8,
   "pad_token_id": null,
   "rms_norm_eps": 1e-05,
-  "rope_theta": 1000000.0,
   "sliding_window": null,
   "tie_word_embeddings": false,
-  "transformers_version": "4.45.0",
   "use_cache": true,
   "vocab_size": 32768
-}

   "num_key_value_heads": 8,
   "pad_token_id": null,
   "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
   "sliding_window": null,
   "tie_word_embeddings": false,
+  "transformers_version": "5.13.1",
   "use_cache": true,
   "vocab_size": 32768
+}