Instructions to use andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3.5-35B-A3B")
model = PeftModel.from_pretrained(base_model, "andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora")

Transformers

How to use andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora with Transformers:

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

pipe = pipeline("text-generation", model="andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora

SGLang

How to use andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora 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 "andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora" \
    --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": "andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora",
		"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 "andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora" \
        --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": "andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora with Docker Model Runner:
```
docker model run hf.co/andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora
```

Qwen3.5-35B-A3B — Telco Track A LoRA Adapter

A LoRA adapter distilled from a DeepSeek-V3-5 teacher on 5G NR drive-test troubleshooting trajectories (Zindi Telco Troubleshooting Agentic Challenge — Track A).

Model Details

Property	Value
Base model	unsloth/Qwen3.5-35B-A3B
Architecture	Qwen3.5 MoE — 35B total params, ~3B active
Adapter type	LoRA (PEFT)
LoRA rank	16
LoRA alpha	32
Target modules	q_proj, k_proj, v_proj, o_proj
Training precision	BF16
Training method	Behavioral cloning / SFT distillation
Teacher model	DeepSeek-V3-5 (deepseek/deepseek-v3-5 via OpenRouter)
Task	5G NR drive-test fault diagnosis (multi-choice)
Adapter size	~33 MB

Training Details

The adapter was trained with Unsloth + TRL SFTTrainer:

Loss masking: assistant turns only (train_on_responses_only)
Distillation: teacher (DeepSeek-V3-5) generated full agentic trajectories (tool calls + reasoning chain + final answer). Only trajectories with IoU ≥ 0.5 against ground truth were kept.
Context window: 4096 tokens
Effective batch: 8 (bs=1, grad_accum=8)
Epochs: 3
Learning rate: 2e-4 (cosine schedule, 5% warmup)
Optimizer: AdamW 8-bit
Hardware: 2× NVIDIA A40

Task Description

Given a 5G NR drive-test scenario (RF measurements, UE logs, network counters), the model uses agentic tool calls to investigate root causes and selects the correct fault categories from a multiple-choice list. Evaluation metric: IoU between predicted and gold answer sets.

How to Load

from unsloth import FastModel
from peft import PeftModel

model, tokenizer = FastModel.from_pretrained(
    model_name="unsloth/Qwen3.5-35B-A3B",
    max_seq_length=4096,
    load_in_4bit=False,
    load_in_16bit=True,
)
model = PeftModel.from_pretrained(model, "andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora")
model.eval()

GGUF Versions

Quantized GGUF files (Q4_K_M and Q5_K_M) are available in the companion repo: andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-GGUF

Framework Versions

PEFT 0.19.1
Unsloth (latest at training time)
TRL SFTTrainer
transformers ≥ 4.47

Downloads last month: 4

Model tree for andreribeiro87/Qwen3.5-35B-A3B-telco-tracka-lora

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen3.5-35B-A3B

Finetuned

unsloth/Qwen3.5-35B-A3B

Adapter

(12)

this model