Instructions to use Nagacharan25/weather-llama-initial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nagacharan25/weather-llama-initial with Transformers:

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

pipe = pipeline("text-generation", model="Nagacharan25/weather-llama-initial")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Nagacharan25/weather-llama-initial")
model = AutoModelForMultimodalLM.from_pretrained("Nagacharan25/weather-llama-initial")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nagacharan25/weather-llama-initial with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nagacharan25/weather-llama-initial"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nagacharan25/weather-llama-initial",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Nagacharan25/weather-llama-initial

SGLang

How to use Nagacharan25/weather-llama-initial 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 "Nagacharan25/weather-llama-initial" \
    --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": "Nagacharan25/weather-llama-initial",
		"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 "Nagacharan25/weather-llama-initial" \
        --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": "Nagacharan25/weather-llama-initial",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Nagacharan25/weather-llama-initial with Docker Model Runner:
```
docker model run hf.co/Nagacharan25/weather-llama-initial
```

Nagacharan25 commited on Mar 24

Commit

01a2a49

verified ·

1 Parent(s): 27f691f

Add model card

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README.md +28 -0

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	@@ -0,0 +1,28 @@

+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - llama
+  - causal-lm
+  - weather
+  - supervised-fine-tuning
+---
+# Weather LLM — SFT (run1 stage2, step 2500)
+Supervised fine-tuning checkpoint: **`run1_stage2/checkpoint-2500`**.
+Tokenizer config adjusted for Hub (`LlamaTokenizer`, `model_max_length=2048`).
+## Load
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+repo = "Nagacharan25/weather-llama-initial"
+model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(repo)
+```
+Requires `transformers` (Llama + SentencePiece) and `sentencepiece`.