初始化项目，由ModelHub XC社区提供模型
Model: NOSIBLE/financial-sentiment-v1.2-base Source: Original Platform
This commit is contained in:
ModelHub XC
36
.gitattributes
vendored
Normal file
36
.gitattributes
vendored
Normal file
@@ -0,0 +1,36 @@
|
||||
*.7z filter=lfs diff=lfs merge=lfs -text
|
||||
*.arrow filter=lfs diff=lfs merge=lfs -text
|
||||
*.bin filter=lfs diff=lfs merge=lfs -text
|
||||
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
||||
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
||||
*.ftz filter=lfs diff=lfs merge=lfs -text
|
||||
*.gz filter=lfs diff=lfs merge=lfs -text
|
||||
*.h5 filter=lfs diff=lfs merge=lfs -text
|
||||
*.joblib filter=lfs diff=lfs merge=lfs -text
|
||||
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
||||
*.model filter=lfs diff=lfs merge=lfs -text
|
||||
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
||||
*.npy filter=lfs diff=lfs merge=lfs -text
|
||||
*.npz filter=lfs diff=lfs merge=lfs -text
|
||||
*.onnx filter=lfs diff=lfs merge=lfs -text
|
||||
*.ot filter=lfs diff=lfs merge=lfs -text
|
||||
*.parquet filter=lfs diff=lfs merge=lfs -text
|
||||
*.pb filter=lfs diff=lfs merge=lfs -text
|
||||
*.pickle filter=lfs diff=lfs merge=lfs -text
|
||||
*.pkl filter=lfs diff=lfs merge=lfs -text
|
||||
*.pt filter=lfs diff=lfs merge=lfs -text
|
||||
*.pth filter=lfs diff=lfs merge=lfs -text
|
||||
*.rar filter=lfs diff=lfs merge=lfs -text
|
||||
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
||||
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
||||
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.tar filter=lfs diff=lfs merge=lfs -text
|
||||
*.tflite filter=lfs diff=lfs merge=lfs -text
|
||||
*.tgz filter=lfs diff=lfs merge=lfs -text
|
||||
*.wasm filter=lfs diff=lfs merge=lfs -text
|
||||
*.xz filter=lfs diff=lfs merge=lfs -text
|
||||
*.zip filter=lfs diff=lfs merge=lfs -text
|
||||
*.zst filter=lfs diff=lfs merge=lfs -text
|
||||
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
||||
tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
||||
327
README.md
Normal file
327
README.md
Normal file
@@ -0,0 +1,327 @@
|
||||
---
|
||||
library_name: transformers
|
||||
license: apache-2.0
|
||||
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
|
||||
pipeline_tag: text-generation
|
||||
base_model:
|
||||
- Qwen/Qwen3-0.6B-Base
|
||||
tags:
|
||||
- financial-sentiment
|
||||
- finance
|
||||
- sentiment-analysis
|
||||
- nlp
|
||||
---
|
||||
|
||||
<p align="center">
|
||||
<img src="https://github.com/NosibleAI/nosible-py/blob/main/docs/_static/readme.png?raw=true"/>
|
||||
<p>
|
||||
|
||||
## Changelog
|
||||
- **v1.2.0:** Multilingual upgrade. Extends coverage to **94 languages** while holding English performance flat, and adds currency / G10-geography coverage.
|
||||
- **v1.1.0:** English financial-sentiment model trained on real-world Nosible Search Feeds.
|
||||
|
||||
**financial-sentiment-v1.2-base** is a financial sentiment classification model built to determine whether a short text snippet describes an event likely to have a **positive**, **neutral**, or **negative** financial impact. It is fine-tuned from [**Qwen3-0.6B-Base**](https://huggingface.co/Qwen/Qwen3-0.6B) and reframes sentiment classification as instruction following, producing a single label token per input.
|
||||
|
||||
This is the **multilingual successor to [financial-sentiment-v1.1-base](https://huggingface.co/NOSIBLE/financial-sentiment-v1.1-base)**. v1.1 was trained primarily on English; v1.2 extends the same task to **94 languages** (English plus 93 additional languages) so the model can classify financial sentiment on text as it appears across global news and search feeds.
|
||||
|
||||
### What's new in v1.2
|
||||
|
||||
- **Multilingual coverage.** The training corpus extends the English [Financial Sentiment](https://huggingface.co/datasets/NOSIBLE/financial-sentiment) data with faithful translations across 93 additional languages, where the financial-sentiment label is preserved through translation (a financially negative snippet stays negative, etc.).
|
||||
- **Wider topic coverage.** v1.2 adds currency and G10-geography feeds, improving sentiment classification on currency- and country / region-focused text, not just company news.
|
||||
- **English held flat.** v1.2 is a multilingual extension, not an English re-train. English accuracy and macro-F1 are unchanged within run noise (see below).
|
||||
- **The multilingual gap roughly halved.** On the held-out validation set, the English-vs-multilingual accuracy gap shrinks from **~11.0pp** (v1.1) to **~4.8pp** (v1.2).
|
||||
|
||||
### Performance overview
|
||||
|
||||
All numbers below are measured on the **live SGLang endpoint** (OpenAI-compatible chat-completions, `enable_thinking=False`, `temperature=0`), scored against the same held-out validation splits for both models. Deltas are in **percentage points (pp)**.
|
||||
|
||||
#### Headline
|
||||
|
||||
| Slice | n | Metric | v1.1 | v1.2 | Δ |
|
||||
|-------|---:|--------|-----:|-----:|----:|
|
||||
| English val | 20,000 | Accuracy | 87.70% | 87.97% | +0.27pp |
|
||||
| English val | 20,000 | Macro-F1 | 87.95% | 88.22% | +0.27pp |
|
||||
| **Multilingual val** | 19,194 | Accuracy | 76.69% | **83.16%** | **+6.47pp** |
|
||||
| **Multilingual val** | 19,194 | Macro-F1 | 76.90% | **83.27%** | **+6.37pp** |
|
||||
| Currency / geo feeds | 4,012 | Accuracy | 67.30% | 76.17% | +8.87pp |
|
||||
| Currency / geo feeds | 4,012 | Macro-F1 | 67.44% | 75.69% | +8.25pp |
|
||||
|
||||
English is held flat while multilingual accuracy improves by **+6.47pp** and the currency / geography feeds improve by **+8.87pp**.
|
||||
|
||||
#### Selected languages (largest validation slices)
|
||||
|
||||
| Language | n | v1.1 acc | v1.2 acc | Δ acc |
|
||||
|----------|---:|--------:|--------:|------:|
|
||||
| German (de) | 1,597 | 82.22% | 87.16% | +4.94pp |
|
||||
| Japanese (ja) | 1,508 | 80.17% | 85.08% | +4.91pp |
|
||||
| Spanish (es) | 1,263 | 82.82% | 86.54% | +3.72pp |
|
||||
| Russian (ru) | 1,686 | 83.75% | 86.89% | +3.14pp |
|
||||
| French (fr) | 1,233 | 84.18% | 86.94% | +2.76pp |
|
||||
| Portuguese (pt) | 730 | 82.60% | 85.07% | +2.47pp |
|
||||
| Italian (it) | 673 | 85.14% | 87.37% | +2.23pp |
|
||||
| Chinese (zh) | 1,383 | 84.24% | 86.12% | +1.88pp |
|
||||
| Polish (pl) | 594 | 78.45% | 85.35% | +6.90pp |
|
||||
| Dutch (nl) | 511 | 77.89% | 83.56% | +5.67pp |
|
||||
|
||||
The gains are largest on lower-resource languages, where v1.1 tended to collapse to the dominant class. For example, accuracy rises on Tamil (40.35% → 73.68%), Hausa (39.39% → 65.66%), and Swahili (44.74% → 63.16%), with even larger macro-F1 improvements as the model recovers per-class signal.
|
||||
|
||||
## Strict Usage Requirements
|
||||
|
||||
> [!CAUTION]
|
||||
> 1. **Disable Thinking:** You **must** set `enable_thinking=False` (or disable reasoning tokens).
|
||||
> 2. **Exact System Prompt:** You **must** use the specific system prompt: `"Classify the financial sentiment as positive, neutral, or negative."`
|
||||
> 3. **Constrain Output:** You **must** restrict generation to the valid labels (`["positive", "neutral", "negative"]`) using **grammars**, **regex**, or **guided decoding**.
|
||||
> * **SGLang:** Use `regex="(positive|neutral|negative)"` in the API call.
|
||||
> * **vLLM:** Use `guided_choice=["positive", "negative", "neutral"]` in the API call.
|
||||
> * **llama.cpp / GGUF:** Apply a GBNF grammar or regex to force selection from the list.
|
||||
>
|
||||
> Deviating from these requirements will **severely** impact performance and reliability.
|
||||
|
||||
## Quickstart (local GPU)
|
||||
|
||||
Since this model was trained as a Causal LM using specific chat templates, you must use `apply_chat_template` with the exact system prompt used during training.
|
||||
|
||||
```python
|
||||
import torch
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
model_id = "NOSIBLE/financial-sentiment-v1.2-base"
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_id,
|
||||
device_map="auto",
|
||||
trust_remote_code=True,
|
||||
torch_dtype=torch.bfloat16,
|
||||
)
|
||||
|
||||
# Multilingual input is supported (94 languages).
|
||||
text = "La empresa reportó un margen de beneficio récord del 15% este trimestre."
|
||||
|
||||
# 1. Structure the prompt exactly as used in training
|
||||
messages = [
|
||||
{"role": "system", "content": "Classify the financial sentiment as positive, neutral, or negative."},
|
||||
{"role": "user", "content": text},
|
||||
]
|
||||
|
||||
# 2. Apply chat template (thinking MUST be disabled)
|
||||
prompt = tokenizer.apply_chat_template(
|
||||
messages,
|
||||
tokenize=False,
|
||||
add_generation_prompt=True,
|
||||
enable_thinking=False,
|
||||
)
|
||||
|
||||
inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
|
||||
|
||||
# 3. Generate the label (only a single token is expected)
|
||||
outputs = model.generate(**inputs, max_new_tokens=1)
|
||||
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
||||
|
||||
print(response.split("<|im_start|>assistant\n")[-1])
|
||||
# Expected Output: positive
|
||||
```
|
||||
|
||||
## Deployment
|
||||
|
||||
For production we recommend serving with [**SGLang**](https://github.com/sgl-project/sglang) (`sglang>=0.4.6.post1`), which exposes an OpenAI-compatible API endpoint. The model is based on Qwen3-0.6B and can be deployed anywhere Qwen3-0.6B can.
|
||||
|
||||
**Launch the server:**
|
||||
|
||||
```shell
|
||||
python3 -m sglang.launch_server --model-path NOSIBLE/financial-sentiment-v1.2-base --dtype bfloat16 --host 0.0.0.0 --port 8080
|
||||
```
|
||||
|
||||
**Call the endpoint** using the OpenAI-compatible client. Requesting `logprobs` lets you read a calibrated confidence for each label.
|
||||
|
||||
```python
|
||||
import math
|
||||
from openai import OpenAI
|
||||
|
||||
# OpenAI-compatible client pointed at your SGLang server (set base_url to your
|
||||
# endpoint URL if remote). The request shape mirrors signals_deploy_v12.predict_one.
|
||||
client = OpenAI(base_url="http://localhost:8080/v1", api_key="EMPTY")
|
||||
|
||||
model_id = "NOSIBLE/financial-sentiment-v1.2-base"
|
||||
|
||||
# Multilingual input is supported.
|
||||
text = "La empresa reportó un margen de beneficio récord del 15% este trimestre."
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": "Classify the financial sentiment as positive, neutral, or negative."},
|
||||
{"role": "user", "content": text},
|
||||
]
|
||||
|
||||
completion = client.chat.completions.create(
|
||||
model=model_id,
|
||||
messages=messages,
|
||||
temperature=0,
|
||||
stream=False,
|
||||
logprobs=True,
|
||||
top_logprobs=3,
|
||||
extra_body={"chat_template_kwargs": {"enable_thinking": False}}, # Must be set to false.
|
||||
)
|
||||
|
||||
# The top-1 token is the predicted label; the full top_logprobs slice gives a
|
||||
# per-label confidence.
|
||||
top = completion.choices[0].logprobs.content[0].top_logprobs
|
||||
print(f"Input: {text}")
|
||||
print(f"Predicted Label: {top[0].token.strip()}")
|
||||
print("--- Label Confidence ---")
|
||||
for lp in top:
|
||||
print(f"Token: {lp.token.strip()!r} | Probability: {math.exp(lp.logprob):.2%}")
|
||||
```
|
||||
|
||||
#### Expected Output
|
||||
|
||||
```text
|
||||
Input: La empresa reportó un margen de beneficio récord del 15% este trimestre.
|
||||
Predicted Label: positive
|
||||
|
||||
--- Label Confidence ---
|
||||
Token: 'positive' | Probability: 99.87%
|
||||
Token: 'neutral' | Probability: 0.11%
|
||||
Token: 'negative' | Probability: 0.02%
|
||||
```
|
||||
|
||||
**Legal Notice:** This model is a modification of the Qwen3-0.6B model. In compliance with the Apache 2.0 license, we retain all original copyright notices and provide this modification under the same license terms.
|
||||
|
||||
## Limitations
|
||||
|
||||
* **Parameter Size (0.6B):** As a small language model, it is designed for fast, specific classification and may struggle with highly nuanced or ambiguous text that requires extensive world knowledge.
|
||||
* **Per-language quality varies.** Accuracy on the highest-resource languages approaches the English baseline; lower-resource languages remain below it despite the large v1.2 improvements.
|
||||
* **Domain Specificity:** The model is fine-tuned on **financial contexts**. It is not suitable for general sentiment analysis (e.g. product reviews).
|
||||
* **Not aspect-based:** The model returns a single, blunt sentiment for the snippet as a whole. It does **not** perform aspect-based sentiment analysis — it will not attribute different sentiments to different entities, companies, or aspects mentioned in the same text.
|
||||
* **Factuality:** The model analyzes the *sentiment* of the text provided; it does not verify the factual accuracy of any figures, dates, or claims within it.
|
||||
|
||||
## Disclaimer
|
||||
|
||||
* **Not Financial Advice:** The outputs of this model **should not be interpreted as financial advice, investment recommendations, or an endorsement** of any financial instrument or asset.
|
||||
* **Risk:** Financial markets are inherently volatile and risky. **Never make investment decisions based solely on the output of an AI model.** Always consult with a qualified financial professional.
|
||||
|
||||
## Team & Credits
|
||||
|
||||
This model was developed and maintained by the following team:
|
||||
|
||||
* [**Matthew Dicks**](https://www.linkedin.com/in/matthewdicks98/)
|
||||
* [**Gareth Warburton**](https://www.linkedin.com/in/garethwarburton/)
|
||||
* [**Stuart Reid**](https://www.linkedin.com/in/stuartgordonreid/)
|
||||
|
||||
## Citation
|
||||
|
||||
If you use this model, please cite it as follows:
|
||||
|
||||
```bibtex
|
||||
@misc{nosible2025financialsentimentv12,
|
||||
author = {NOSIBLE},
|
||||
title = {Financial Sentiment v1.2 Base},
|
||||
year = {2025},
|
||||
publisher = {Hugging Face},
|
||||
journal = {Hugging Face Repository},
|
||||
howpublished = {https://huggingface.co/NOSIBLE/financial-sentiment-v1.2-base}
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
## Full language breakdown
|
||||
|
||||
v1.1 was trained on **English only**; v1.2 adds the **93 languages** below (94 total
|
||||
with English). The figures are the training-time evaluation per language and reproduce
|
||||
on the served SGLang endpoint to within ~0.2pp. Deltas are in percentage points (pp),
|
||||
sorted by validation row count.
|
||||
|
||||
| Language | n | v1.1 acc | v1.2 acc | Δ acc | v1.1 F1 | v1.2 F1 | Δ F1 |
|
||||
|----------|---:|--------:|--------:|------:|-------:|-------:|------:|
|
||||
| Russian (ru) | 1,686 | 83.63% | 86.83% | +3.20pp | 83.91% | 86.88% | +2.97pp |
|
||||
| German (de) | 1,597 | 82.15% | 87.16% | +5.01pp | 82.25% | 87.50% | +5.25pp |
|
||||
| Japanese (ja) | 1,507 | 80.36% | 85.14% | +4.78pp | 80.50% | 85.59% | +5.09pp |
|
||||
| Chinese (zh) | 1,382 | 84.30% | 86.25% | +1.95pp | 84.59% | 86.49% | +1.90pp |
|
||||
| Spanish (es) | 1,263 | 82.90% | 86.38% | +3.48pp | 82.91% | 86.57% | +3.66pp |
|
||||
| French (fr) | 1,233 | 84.02% | 86.86% | +2.84pp | 84.81% | 87.55% | +2.74pp |
|
||||
| Portuguese (pt) | 730 | 82.60% | 85.21% | +2.61pp | 82.96% | 85.58% | +2.62pp |
|
||||
| Italian (it) | 673 | 85.44% | 87.37% | +1.93pp | 85.54% | 87.75% | +2.21pp |
|
||||
| Polish (pl) | 594 | 78.45% | 85.52% | +7.07pp | 78.61% | 85.37% | +6.76pp |
|
||||
| Dutch (nl) | 511 | 78.28% | 83.76% | +5.48pp | 79.13% | 84.28% | +5.15pp |
|
||||
| Turkish (tr) | 393 | 79.13% | 81.93% | +2.80pp | 78.78% | 81.71% | +2.93pp |
|
||||
| Indonesian (id) | 383 | 80.94% | 86.42% | +5.48pp | 80.99% | 86.62% | +5.63pp |
|
||||
| Vietnamese (vi) | 366 | 83.06% | 82.51% | -0.55pp | 83.82% | 82.83% | -0.99pp |
|
||||
| Czech (cs) | 331 | 78.55% | 84.59% | +6.04pp | 78.62% | 84.18% | +5.56pp |
|
||||
| Korean (ko) | 291 | 80.41% | 84.54% | +4.13pp | 81.46% | 85.04% | +3.58pp |
|
||||
| Arabic (ar) | 288 | 78.82% | 82.29% | +3.47pp | 77.98% | 82.15% | +4.17pp |
|
||||
| Ukrainian (uk) | 247 | 79.76% | 84.21% | +4.45pp | 80.32% | 84.52% | +4.20pp |
|
||||
| Swedish (sv) | 228 | 81.14% | 84.65% | +3.51pp | 80.61% | 84.71% | +4.10pp |
|
||||
| Romanian (ro) | 227 | 78.85% | 84.58% | +5.73pp | 79.82% | 85.19% | +5.37pp |
|
||||
| Hindi (hi) | 190 | 68.42% | 80.53% | +12.11pp | 66.87% | 80.80% | +13.93pp |
|
||||
| Greek (el) | 187 | 67.91% | 77.54% | +9.63pp | 64.64% | 76.79% | +12.15pp |
|
||||
| Hungarian (hu) | 178 | 72.47% | 81.46% | +8.99pp | 72.05% | 81.06% | +9.01pp |
|
||||
| Thai (th) | 178 | 75.84% | 87.64% | +11.80pp | 77.31% | 88.06% | +10.75pp |
|
||||
| Danish (da) | 177 | 79.66% | 84.75% | +5.09pp | 78.53% | 83.92% | +5.39pp |
|
||||
| Bengali (bn) | 145 | 62.07% | 75.86% | +13.79pp | 57.80% | 75.14% | +17.34pp |
|
||||
| Slovak (sk) | 145 | 76.55% | 81.38% | +4.83pp | 75.76% | 81.08% | +5.32pp |
|
||||
| Malay (ms) | 143 | 81.12% | 83.22% | +2.10pp | 81.49% | 83.97% | +2.48pp |
|
||||
| Persian (fa) | 136 | 75.74% | 79.41% | +3.67pp | 74.98% | 79.14% | +4.16pp |
|
||||
| Finnish (fi) | 136 | 64.71% | 79.41% | +14.70pp | 62.60% | 79.55% | +16.95pp |
|
||||
| Urdu (ur) | 121 | 71.07% | 76.03% | +4.96pp | 68.50% | 73.49% | +4.99pp |
|
||||
| Norwegian (no) | 115 | 76.52% | 82.61% | +6.09pp | 77.06% | 82.95% | +5.89pp |
|
||||
| Swahili (sw) | 114 | 44.74% | 62.28% | +17.54pp | 28.99% | 61.13% | +32.14pp |
|
||||
| Tamil (ta) | 114 | 40.35% | 73.68% | +33.33pp | 31.49% | 72.14% | +40.65pp |
|
||||
| Serbian (sr) | 113 | 83.19% | 86.73% | +3.54pp | 83.84% | 87.42% | +3.58pp |
|
||||
| Hebrew (he) | 110 | 77.27% | 82.73% | +5.46pp | 76.88% | 82.95% | +6.07pp |
|
||||
| Marathi (mr) | 110 | 50.00% | 80.00% | +30.00pp | 48.89% | 79.98% | +31.09pp |
|
||||
| Bulgarian (bg) | 108 | 84.26% | 87.96% | +3.70pp | 83.11% | 86.97% | +3.86pp |
|
||||
| Punjabi (pa) | 107 | 55.14% | 75.70% | +20.56pp | 51.59% | 74.99% | +23.40pp |
|
||||
| Telugu (te) | 103 | 42.72% | 80.58% | +37.86pp | 32.33% | 80.19% | +47.86pp |
|
||||
| Hausa (ha) | 99 | 39.39% | 64.65% | +25.26pp | 25.76% | 63.19% | +37.43pp |
|
||||
| Tagalog (tl) | 99 | 73.74% | 77.78% | +4.04pp | 72.53% | 77.42% | +4.89pp |
|
||||
| Gujarati (gu) | 98 | 53.06% | 78.57% | +25.51pp | 46.11% | 75.81% | +29.70pp |
|
||||
| Kannada (kn) | 92 | 51.09% | 72.83% | +21.74pp | 38.24% | 72.06% | +33.82pp |
|
||||
| Croatian (hr) | 90 | 76.67% | 81.11% | +4.44pp | 73.00% | 80.94% | +7.94pp |
|
||||
| Azerbaijani (az) | 88 | 69.32% | 75.00% | +5.68pp | 60.86% | 71.82% | +10.96pp |
|
||||
| Pashto (ps) | 88 | 45.45% | 69.32% | +23.87pp | 34.32% | 68.89% | +34.57pp |
|
||||
| Malayalam (ml) | 86 | 47.67% | 72.09% | +24.42pp | 38.68% | 72.09% | +33.41pp |
|
||||
| Nepali (ne) | 84 | 64.29% | 76.19% | +11.90pp | 62.22% | 76.41% | +14.19pp |
|
||||
| Uzbek (uz) | 84 | 54.76% | 73.81% | +19.05pp | 48.65% | 73.47% | +24.82pp |
|
||||
| Burmese (my) | 83 | 48.19% | 69.88% | +21.69pp | 36.83% | 70.75% | +33.92pp |
|
||||
| Odia (or) | 82 | 43.90% | 69.51% | +25.61pp | 32.05% | 68.73% | +36.68pp |
|
||||
| Amharic (am) | 77 | 42.86% | 66.23% | +23.37pp | 23.46% | 59.46% | +36.00pp |
|
||||
| Kazakh (kk) | 77 | 57.14% | 79.22% | +22.08pp | 50.37% | 78.74% | +28.37pp |
|
||||
| Somali (so) | 77 | 53.25% | 51.95% | -1.30pp | 33.35% | 50.88% | +17.53pp |
|
||||
| Sindhi (sd) | 75 | 65.33% | 69.33% | +4.00pp | 63.63% | 67.47% | +3.84pp |
|
||||
| Lithuanian (lt) | 73 | 63.01% | 71.23% | +8.22pp | 58.04% | 69.78% | +11.74pp |
|
||||
| Sinhala (si) | 69 | 40.58% | 52.17% | +11.59pp | 23.58% | 45.35% | +21.77pp |
|
||||
| Assamese (as) | 66 | 57.58% | 80.30% | +22.72pp | 54.79% | 79.92% | +25.13pp |
|
||||
| Khmer (km) | 66 | 63.64% | 69.70% | +6.06pp | 57.87% | 67.45% | +9.58pp |
|
||||
| Slovenian (sl) | 66 | 69.70% | 77.27% | +7.57pp | 63.82% | 73.55% | +9.73pp |
|
||||
| Afrikaans (af) | 64 | 73.44% | 84.38% | +10.94pp | 73.38% | 84.64% | +11.26pp |
|
||||
| Armenian (hy) | 56 | 57.14% | 67.86% | +10.72pp | 51.87% | 66.44% | +14.57pp |
|
||||
| Kyrgyz (ky) | 48 | 43.75% | 77.08% | +33.33pp | 39.29% | 77.76% | +38.47pp |
|
||||
| Latvian (lv) | 48 | 62.50% | 75.00% | +12.50pp | 56.46% | 74.84% | +18.38pp |
|
||||
| Mongolian (mn) | 46 | 47.83% | 78.26% | +30.43pp | 31.94% | 75.92% | +43.98pp |
|
||||
| Lao (lo) | 44 | 68.18% | 70.45% | +2.27pp | 68.80% | 68.63% | -0.17pp |
|
||||
| Georgian (ka) | 41 | 41.46% | 75.61% | +34.15pp | 37.59% | 71.54% | +33.95pp |
|
||||
| Sanskrit (sa) | 22 | 68.18% | 81.82% | +13.64pp | 65.56% | 78.89% | +13.33pp |
|
||||
| Catalan (ca) | 21 | 61.90% | 85.71% | +23.81pp | 63.83% | 86.25% | +22.42pp |
|
||||
| Bosnian (bs) | 20 | 75.00% | 85.00% | +10.00pp | 74.64% | 83.87% | +9.23pp |
|
||||
| Irish (ga) | 20 | 35.00% | 55.00% | +20.00pp | 17.28% | 54.43% | +37.15pp |
|
||||
| Malagasy (mg) | 20 | 50.00% | 75.00% | +25.00pp | 29.76% | 73.26% | +43.50pp |
|
||||
| Welsh (cy) | 19 | 36.84% | 78.95% | +42.11pp | 23.33% | 76.67% | +53.34pp |
|
||||
| Macedonian (mk) | 19 | 89.47% | 89.47% | +0.00pp | 91.07% | 91.07% | +0.00pp |
|
||||
| Belarusian (be) | 18 | 61.11% | 72.22% | +11.11pp | 54.56% | 64.59% | +10.03pp |
|
||||
| Basque (eu) | 18 | 33.33% | 72.22% | +38.89pp | 16.67% | 71.39% | +54.72pp |
|
||||
| Latin (la) | 18 | 55.56% | 88.89% | +33.33pp | 37.78% | 81.75% | +43.97pp |
|
||||
| Serbo-Croatian (sh) | 18 | 83.33% | 88.89% | +5.56pp | 82.44% | 88.97% | +6.53pp |
|
||||
| Yiddish (yi) | 18 | 44.44% | 33.33% | -11.11pp | 20.51% | 42.91% | +22.40pp |
|
||||
| Scottish Gaelic (gd) | 17 | 52.94% | 58.82% | +5.88pp | 23.08% | 55.58% | +32.50pp |
|
||||
| Galician (gl) | 17 | 82.35% | 88.24% | +5.89pp | 81.10% | 84.72% | +3.62pp |
|
||||
| Icelandic (is) | 17 | 47.06% | 64.71% | +17.65pp | 34.21% | 47.22% | +13.01pp |
|
||||
| Oromo (om) | 17 | 41.18% | 58.82% | +17.64pp | 19.44% | 53.53% | +34.09pp |
|
||||
| Xhosa (xh) | 17 | 35.29% | 47.06% | +11.77pp | 17.39% | 33.33% | +15.94pp |
|
||||
| Breton (br) | 16 | 37.50% | 68.75% | +31.25pp | 35.56% | 70.56% | +35.00pp |
|
||||
| Estonian (et) | 16 | 68.75% | 62.50% | -6.25pp | 67.97% | 63.57% | -4.40pp |
|
||||
| Western Frisian (fy) | 16 | 68.75% | 62.50% | -6.25pp | 68.81% | 62.63% | -6.18pp |
|
||||
| Javanese (jv) | 16 | 81.25% | 87.50% | +6.25pp | 77.46% | 82.37% | +4.91pp |
|
||||
| Kurdish (ku) | 16 | 68.75% | 75.00% | +6.25pp | 47.62% | 82.14% | +34.52pp |
|
||||
| Albanian (sq) | 16 | 68.75% | 81.25% | +12.50pp | 60.00% | 80.94% | +20.94pp |
|
||||
| Sundanese (su) | 16 | 68.75% | 68.75% | +0.00pp | 71.31% | 72.03% | +0.72pp |
|
||||
| Uyghur (ug) | 16 | 50.00% | 68.75% | +18.75pp | 22.22% | 48.81% | +26.59pp |
|
||||
| Esperanto (eo) | 15 | 93.33% | 66.67% | -26.66pp | 91.58% | 67.74% | -23.84pp |
|
||||
89
chat_template.jinja
Normal file
89
chat_template.jinja
Normal file
@@ -0,0 +1,89 @@
|
||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- messages[0].content + '\n\n' }}
|
||||
{%- endif %}
|
||||
{{- "# 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' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||
{%- for message in messages[::-1] %}
|
||||
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
||||
{%- set ns.multi_step_tool = false %}
|
||||
{%- set ns.last_query_index = index %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- for message in messages %}
|
||||
{%- if message.content is string %}
|
||||
{%- set content = message.content %}
|
||||
{%- else %}
|
||||
{%- set content = '' %}
|
||||
{%- endif %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{%- set reasoning_content = '' %}
|
||||
{%- if message.reasoning_content is string %}
|
||||
{%- set reasoning_content = message.reasoning_content %}
|
||||
{%- else %}
|
||||
{%- if '</think>' in content %}
|
||||
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
||||
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if loop.index0 > ns.last_query_index %}
|
||||
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- 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' }}
|
||||
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||
{{- '<think>\n\n</think>\n\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
65
config.json
Normal file
65
config.json
Normal file
@@ -0,0 +1,65 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"dtype": "bfloat16",
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 1024,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 3072,
|
||||
"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"
|
||||
],
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": null,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 1000000,
|
||||
"rope_type": "default"
|
||||
},
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"transformers_version": "5.6.0",
|
||||
"use_cache": false,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936,
|
||||
"rope_theta": 1000000,
|
||||
"rope_scaling": null
|
||||
}
|
||||
10
generation_config.json
Normal file
10
generation_config.json
Normal file
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": false,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"pad_token_id": 151643,
|
||||
"transformers_version": "5.6.0"
|
||||
}
|
||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a1b810ddec66587a77fbd820dbd30d199b866841ab3b73b73b1ef6a00cc2493e
|
||||
size 1192135096
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
|
||||
size 11422650
|
||||
242
tokenizer_config.json
Normal file
242
tokenizer_config.json
Normal file
@@ -0,0 +1,242 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"is_local": false,
|
||||
"local_files_only": false,
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"add_bos_token": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_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|>"
|
||||
]
|
||||
}
|
||||
Reference in New Issue
Block a user