Instructions to use c-mohanraj/qwen14b-multi-turn-R2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use c-mohanraj/qwen14b-multi-turn-R2 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("RezolveAI/brainpowa-general-conversational-M-v1")
model = PeftModel.from_pretrained(base_model, "c-mohanraj/qwen14b-multi-turn-R2")

Transformers

How to use c-mohanraj/qwen14b-multi-turn-R2 with Transformers:

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

pipe = pipeline("text-generation", model="c-mohanraj/qwen14b-multi-turn-R2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("c-mohanraj/qwen14b-multi-turn-R2", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use c-mohanraj/qwen14b-multi-turn-R2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "c-mohanraj/qwen14b-multi-turn-R2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "c-mohanraj/qwen14b-multi-turn-R2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/c-mohanraj/qwen14b-multi-turn-R2

SGLang

How to use c-mohanraj/qwen14b-multi-turn-R2 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 "c-mohanraj/qwen14b-multi-turn-R2" \
    --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": "c-mohanraj/qwen14b-multi-turn-R2",
		"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 "c-mohanraj/qwen14b-multi-turn-R2" \
        --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": "c-mohanraj/qwen14b-multi-turn-R2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use c-mohanraj/qwen14b-multi-turn-R2 with Docker Model Runner:
```
docker model run hf.co/c-mohanraj/qwen14b-multi-turn-R2
```

qwen14b-multi-turn-R2

This model is a fine-tuned version of RezolveAI/brainpowa-general-conversational-M-v1 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1976

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.6472	0.1647	200	0.6514
0.5717	0.3295	400	0.5779
0.5236	0.4942	600	0.5135
0.4373	0.6590	800	0.4592
0.4455	0.8237	1000	0.4062
0.4116	0.9885	1200	0.3598
0.2866	1.1532	1400	0.3276
0.2412	1.3180	1600	0.3039
0.2561	1.4827	1800	0.2785
0.2001	1.6474	2000	0.2585
0.1716	1.8122	2200	0.2400
0.1872	1.9769	2400	0.2259
0.1347	2.1417	2600	0.2231
0.153	2.3064	2800	0.2168
0.1281	2.4712	3000	0.2102
0.1226	2.6359	3200	0.2051
0.1298	2.8007	3400	0.1993
0.1161	2.9654	3600	0.1976

Framework versions

PEFT 0.17.1
Transformers 4.56.2
Pytorch 2.6.0+cu124
Datasets 4.1.1
Tokenizers 0.22.1

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