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Model: LaboAI/LaboAI-0.4.0-3B Source: Original Platform
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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Qwen2.5-Coder-3B-Instruct.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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54
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FROM Qwen2.5-Coder-3B-Instruct.Q4_K_M.gguf
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TEMPLATE """{{- if .Suffix }}<|fim_prefix|>{{ .Prompt }}<|fim_suffix|>{{ .Suffix }}<|fim_middle|>
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{{- else if .Messages }}
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{{- if or .System .Tools }}<|im_start|>system
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{{- if .System }}
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{{ .System }}
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{{- end }}
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{{- if .Tools }}
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# Tools
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||||
|
||||
You may call one or more functions to assist with the user query.
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You are provided with function signatures within <tools></tools>:
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<tools>
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{{- range .Tools }}
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{"type": "function", "function": {{ .Function }}}
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{{- end }}
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</tools>
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For each function call, return a json object with function name and arguments within <tool_call></tool_call> with NO other text. Do not include any backticks or ```json.
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<tool_call>
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{"name": <function-name>, "arguments": <args-json-object>}
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</tool_call>
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{{- end }}<|im_end|>
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{{ end }}
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{{- range $i, $_ := .Messages }}
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{{- $last := eq (len (slice $.Messages $i)) 1 -}}
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{{- if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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{{ else if eq .Role "assistant" }}<|im_start|>assistant
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{{ if .Content }}{{ .Content }}
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{{- else if .ToolCalls }}<tool_call>
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{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
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{{ end }}</tool_call>
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{{- end }}{{ if not $last }}<|im_end|>
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{{ end }}
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{{- else if eq .Role "tool" }}<|im_start|>user
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<tool_response>
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{{ .Content }}
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</tool_response><|im_end|>
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{{ end }}
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{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
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{{ end }}
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{{- end }}
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{{- else }}
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{{- if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ if .Prompt }}<|im_start|>user
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{{ .Prompt }}<|im_end|>
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{{ end }}<|im_start|>assistant
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{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
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SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""
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3
Qwen2.5-Coder-3B-Instruct.Q4_K_M.gguf
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Qwen2.5-Coder-3B-Instruct.Q4_K_M.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:264bc8c65979d862af67674a4e0fe245512176ccecae90901e5a55807bd990e7
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size 1929902656
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156
README.md
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README.md
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---
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license: other
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license_name: qwen-research
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license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE
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base_model: Qwen/Qwen2.5-Coder-3B-Instruct
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library_name: transformers
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pipeline_tag: text-generation
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language:
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- en
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- es
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tags:
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- qwen2
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- code
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- kotlin
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- android
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- jetpack-compose
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- unsloth
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- lora
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- gguf
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datasets:
|
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- giggiovpg/ornith-android-instruct
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- giggiovpg/android-kotlin-compose-compiler-verified
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- microsoft/NextCoderDataset
|
||||
- glaiveai/glaive-code-assistant-v3
|
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- theblackcat102/evol-codealpaca-v1
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- sahil2801/CodeAlpaca-20k
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- genqa/GenQA
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---
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||||
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# LaboAI-0.4.0-3B
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||||
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||||
LaboAI-0.4.0-3B is a 3B-parameter code model fine-tuned for **Android / Kotlin / Jetpack Compose** development. It is built on top of [Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct) using QLoRA with [Unsloth](https://github.com/unslothai/unsloth), and the LoRA weights are merged into the released model.
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||||
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||||
## Model details
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||||
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||||
| | |
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||||
|---|---|
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||||
| **Developer** | LaboAI |
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||||
| **Base model** | Qwen/Qwen2.5-Coder-3B-Instruct |
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||||
| **Parameters** | ~3B |
|
||||
| **Fine-tuning method** | QLoRA (4-bit base, LoRA r=32, alpha=64) |
|
||||
| **Context used in training** | 1024 tokens |
|
||||
| **Languages** | Kotlin, Java, general code; English and Spanish prompts |
|
||||
| **Available formats** | Merged safetensors, GGUF `q4_k_m` |
|
||||
|
||||
## Intended use
|
||||
|
||||
- Generating and explaining Kotlin code for Android apps
|
||||
- Jetpack Compose UI snippets, ViewModels, state handling, Room, Retrofit, coroutines
|
||||
- Fixing and refactoring small to medium Kotlin snippets
|
||||
- Local inference on modest hardware via the GGUF build (llama.cpp, Ollama, LM Studio)
|
||||
|
||||
## Prompt format
|
||||
|
||||
The model was trained with a plain instruction/response template (not the Qwen chat template), so for best results use:
|
||||
|
||||
```
|
||||
### Instruction:
|
||||
{your request}
|
||||
|
||||
### Response:
|
||||
```
|
||||
|
||||
## How to use
|
||||
|
||||
### Transformers
|
||||
|
||||
```python
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||||
from transformers import AutoTokenizer, AutoModelForCausalLM
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||||
import torch
|
||||
|
||||
repo = "LaboAI/LaboAI-0.4.0-3B"
|
||||
tokenizer = AutoTokenizer.from_pretrained(repo)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
repo, torch_dtype=torch.float16, device_map="auto"
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||||
)
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||||
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||||
prompt = (
|
||||
"### Instruction:\n"
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||||
"Write a Jetpack Compose screen with a counter and a button to increment it.\n\n"
|
||||
"### Response:\n"
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||||
)
|
||||
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=512, temperature=0.2, do_sample=True)
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print(tokenizer.decode(out[0], skip_special_tokens=True))
|
||||
```
|
||||
|
||||
### llama.cpp / GGUF
|
||||
|
||||
```bash
|
||||
llama-cli -m LaboAI-0.4.0-3B-Q4_K_M.gguf -c 2048 --temp 0.2 \
|
||||
-p "### Instruction:\nCreate a Room DAO for a Note entity.\n\n### Response:\n"
|
||||
```
|
||||
|
||||
## Training data
|
||||
|
||||
The training mix was filtered for Android/Kotlin/Jetpack content (keyword filter on the text fields) and formatted into the instruction/response template above:
|
||||
|
||||
| Dataset | Notes |
|
||||
|---|---|
|
||||
| giggiovpg/ornith-android-instruct | Android instruct data |
|
||||
| giggiovpg/android-kotlin-compose-compiler-verified | Compose code verified by compiler |
|
||||
| microsoft/NextCoderDataset | Kotlin subset, capped at 10,000 |
|
||||
| glaiveai/glaive-code-assistant-v3 | Android/Kotlin subset, capped at 6,000 |
|
||||
| theblackcat102/evol-codealpaca-v1 | Android/Kotlin subset |
|
||||
| sahil2801/CodeAlpaca-20k | Android/Kotlin subset |
|
||||
| genqa/GenQA (code split) | Android/Kotlin subset, capped at 4,000 |
|
||||
|
||||
Examples with empty or near-empty text were removed, and the final dataset was shuffled (seed 42).
|
||||
|
||||
## Training procedure
|
||||
|
||||
| Hyperparameter | Value |
|
||||
|---|---|
|
||||
| LoRA rank / alpha / dropout | 32 / 64 / 0 |
|
||||
| Target modules | q, k, v, o, gate, up, down projections |
|
||||
| Max sequence length | 1024 |
|
||||
| Per-device batch size | 2 |
|
||||
| Gradient accumulation | 4 (effective batch 8) |
|
||||
| Steps | 4,500 |
|
||||
| Warmup steps | 225 |
|
||||
| Learning rate | 1e-4 (cosine) |
|
||||
| Weight decay | 0.01 |
|
||||
| Optimizer | AdamW 8-bit |
|
||||
| Precision | fp16 (T4) |
|
||||
| Hardware | 1x NVIDIA T4 (Kaggle) |
|
||||
| Framework | Unsloth + TRL SFTTrainer |
|
||||
|
||||
## Limitations and risks
|
||||
|
||||
- It is a small 3B model: it can produce code that does not compile, uses deprecated or non-existent APIs, or contains subtle bugs. Always review and test the output.
|
||||
- Trained with a 1024-token context, so very long files or multi-file projects may degrade quality.
|
||||
- The keyword-based filtering may have let in some off-topic examples.
|
||||
- Android and Compose APIs change quickly; the model may not know the latest library versions.
|
||||
- Not evaluated on formal benchmarks yet; no quantitative results are claimed.
|
||||
|
||||
## License
|
||||
|
||||
This model is derived from Qwen2.5-Coder-3B-Instruct and inherits its license (Qwen Research License). Please check the base model's license for terms of use, especially for commercial use. The training datasets have their own licenses, which you should also review.
|
||||
|
||||
## Acknowledgements
|
||||
|
||||
- [Qwen team](https://huggingface.co/Qwen) for the base model
|
||||
- [Unsloth](https://github.com/unslothai/unsloth) for efficient fine-tuning
|
||||
- The authors of the datasets listed above
|
||||
|
||||
## Citation
|
||||
|
||||
```bibtex
|
||||
@misc{laboai2026,
|
||||
title = {LaboAI-0.4.0-3B: a Kotlin/Android code model},
|
||||
author = {LaboAI},
|
||||
year = {2026},
|
||||
url = {https://huggingface.co/LaboAI/LaboAI-0.4.0-3B}
|
||||
}
|
||||
```
|
||||
54
chat_template.jinja
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54
chat_template.jinja
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{%- if tools %}
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||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- messages[0]['content'] }}
|
||||
{%- else %}
|
||||
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
||||
{%- endif %}
|
||||
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- if message.content %}
|
||||
{{- '\n' + message.content }}
|
||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- endif %}
|
||||
69
config.json
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config.json
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|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"torch_dtype": "bfloat16",
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2048,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 11008,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 70,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 2,
|
||||
"pad_token_id": 151665,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 1000000.0,
|
||||
"rope_type": "default"
|
||||
},
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"unsloth_fixed": true,
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
14
generation_config.json
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generation_config.json
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|
||||
{
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"max_length": 32768,
|
||||
"pad_token_id": 151665,
|
||||
"repetition_penalty": 1.05,
|
||||
"temperature": 0.7,
|
||||
"top_k": 20,
|
||||
"top_p": 0.8,
|
||||
"transformers_version": "5.17.0"
|
||||
}
|
||||
3
model-00001-of-00002.safetensors
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model-00001-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:919807c798263b7088bd08b5529d5aee09b46dc1179339e8e4621d9942f79d3a
|
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size 4957560304
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3
model-00002-of-00002.safetensors
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3
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
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|
||||
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|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||
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|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
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|
||||
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|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:01c060135db283a7f3530c39cddd55bbac96eeb2ba8a6da6c8a103f47c8be824
|
||||
size 11422455
|
||||
218
tokenizer_config.json
Normal file
218
tokenizer_config.json
Normal file
@@ -0,0 +1,218 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"is_local": false,
|
||||
"local_files_only": false,
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|PAD_TOKEN|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<|PAD_TOKEN|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
|
||||
}
|
||||
Reference in New Issue
Block a user