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Qwen3.8-2B-Function-Calling-xLAM-GGUF
tinyopsec/Qwen3.8-2B-Function-Calling-xLAM-GGUF
Qwen3.8-2B-Function-Calling-xLAM-GGUF is a machine learning model from tinyopsec. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
GGUF quantizations of ermiaazarkhalili/Qwen3.8-2B-Function-Calling-xLAM-Unsloth, a 2B parameter model fine-tuned for function calling and tool use via the xLAM pipeline.
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Repo size
18.6 GB
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.gguf18.6 GB · 100%
At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Aug 30, 2026
- Updated
- Aug 30, 2026
- SHA
- 59e7eaa6
Base models
Model card
From the Hugging Face model README
Qwen3.8-2B-Function-Calling-xLAM GGUF
GGUF quantizations of ermiaazarkhalili/Qwen3.8-2B-Function-Calling-xLAM-Unsloth, a 2B parameter model fine-tuned for function calling and tool use via the xLAM pipeline.
Model Architecture
| Property | Value |
|---|---|
| Architecture | Qwen3.5 (SSM + Attention hybrid) |
| Parameters | 2B |
| Context Length | 262144 tokens |
| Base Model | Qwen3.8-2B |
Quantizations
| Quantization | File Size | Description |
|---|---|---|
| Q8_0 | 2.08 GB | Near-lossless, maximum quality |
| Q6_K | 1.61 GB | Very high quality |
| Q5_K_M | 1.45 GB | High quality |
| Q5_K_S | 1.42 GB | High quality, smaller |
| Q4_K_M | 1.31 GB | Recommended, good balance |
| Q4_K_S | 1.25 GB | Good quality, smaller |
| IQ4_XS | 1.23 GB | Good quality, imatrix calibrated |
| Q3_K_L | 1.20 GB | Medium quality |
| Q3_K_M | 1.13 GB | Medium quality |
| Q3_K_S | 1.05 GB | Below average quality |
| Q2_K | 990 MB | Low quality, smallest |
Q4_K_M is recommended for most use cases.
Usage
Download a single file with huggingface-cli:
huggingface-cli download tinyopsec/Qwen3.8-2B-Function-Calling-xLAM-GGUF --include "Q4_K_M.gguf" --local-dir ./
Run with llama.cpp:
llama-cli -m Q4_K_M.gguf -p "Your prompt here" -n 512
Run as a server:
llama-server -m Q4_K_M.gguf --port 8080
Chat Template
This model uses the Qwen3 chat template:
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
Conversion Details
Quantizations were produced using llama.cpp. The intermediate F16 GGUF was created with convert_hf_to_gguf.py and K-quants were produced with llama-quantize. IQ4_XS was produced using an imatrix calibration file.
Original Model
- License
- apache-2.0
- Languages
- en
- Created
- Aug 30, 2026
- Updated
- Aug 30, 2026