Instructions to use RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM with Transformers:

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

pipe = pipeline("text-generation", model="RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM")
model = AutoModelForCausalLM.from_pretrained("RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM")
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 RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM

SGLang

How to use RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM 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 "RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM" \
    --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": "RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM",
		"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 "RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM" \
        --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": "RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM with Docker Model Runner:
```
docker model run hf.co/RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen2.5-Coder-RASA-Calm

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-1.5B-Instruct on the RASA Calm dataset for improved command generation and intent understanding.

Model Description

This model extends Qwen2.5-Coder's capabilities by specializing in natural language understanding and command generation for conversational AI applications, particularly in the context of the RASA framework.

Training Data

The model was fine-tuned on the RASA Calm demonstration dataset (rasa/command-generation-calm-demo-v1), which contains examples of:

Natural language user inputs Corresponding intents and entities Generated command structures

Model Architecture

Base model: Qwen2.5-Coder-1.5B-Instruct Architecture: Transformer-based Parameters: 1.5B Context window: 8192 tokens

Intended Use

This model is designed for:

Converting natural language inputs into RASA-compatible commands
Understanding user intents in conversational AI applications
Generating structured outputs for chatbot development

Limitations

The model's performance is optimized for the RASA framework and may not generalize well to other command generation tasks
Limited to the scope of intents and entities present in the CALM dataset
Inherits any limitations present in the base Qwen2.5-Coder model

Downloads last month: 3

Safetensors

Model size

2B params

Tensor type

F16

Model tree for RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B-Instruct

Finetuned

(155)

this model

Quantizations

1 model

RakshitAralimatti
/

Qwen2.5-Coder-1.5B-Instruct-RASA-CALM

Qwen2.5-Coder-RASA-Calm

Model Description

Training Data

Model Architecture

Intended Use

Limitations

Model tree for RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM

Dataset used to train RakshitAralimatti/Qwen2.5-Coder-1.5B-Instruct-RASA-CALM