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Model: Ayansk11/FinSenti-Qwen3-1.7B-GGUF Source: Original Platform
2026-05-01 07:52:45 +08:00
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+FinSenti-Qwen3-1.7B.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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--- a/Modelfile.Q4_K_M
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+FROM ./FinSenti-Qwen3-1.7B.Q4_K_M.gguf
+
+TEMPLATE \"\"\"<|im_start|>system
+You are a financial sentiment analyst. Analyze the given financial text and provide:
+1. Your reasoning in <reasoning> tags
+2. Your sentiment classification (positive, negative, or neutral) in <answer> tags
+
+Always use this exact format:
+<reasoning>
+[Your step-by-step analysis]
+</reasoning>
+<answer>[positive/negative/neutral]</answer><|im_end|>
+<|im_start|>user
+{{ .Prompt }}<|im_end|>
+<|im_start|>assistant
+<think>
+</think>
+
+\"\"\"
+
+PARAMETER stop "<|im_end|>"
+PARAMETER stop "</answer>"
+PARAMETER stop "<|endoftext|>"
+PARAMETER stop "<|im_start|>"
+
+PARAMETER temperature 0.3
+PARAMETER top_p 0.9
+PARAMETER top_k 40
+PARAMETER repeat_penalty 1.15
+PARAMETER num_ctx 1024
+PARAMETER num_predict 512
--- a/Modelfile.Q5_K_M
+++ b/Modelfile.Q5_K_M
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+FROM ./FinSenti-Qwen3-1.7B.Q5_K_M.gguf
+
+TEMPLATE \"\"\"<|im_start|>system
+You are a financial sentiment analyst. Analyze the given financial text and provide:
+1. Your reasoning in <reasoning> tags
+2. Your sentiment classification (positive, negative, or neutral) in <answer> tags
+
+Always use this exact format:
+<reasoning>
+[Your step-by-step analysis]
+</reasoning>
+<answer>[positive/negative/neutral]</answer><|im_end|>
+<|im_start|>user
+{{ .Prompt }}<|im_end|>
+<|im_start|>assistant
+<think>
+</think>
+
+\"\"\"
+
+PARAMETER stop "<|im_end|>"
+PARAMETER stop "</answer>"
+PARAMETER stop "<|endoftext|>"
+PARAMETER stop "<|im_start|>"
+
+PARAMETER temperature 0.3
+PARAMETER top_p 0.9
+PARAMETER top_k 40
+PARAMETER repeat_penalty 1.15
+PARAMETER num_ctx 1024
+PARAMETER num_predict 512
--- a/Modelfile.Q8_0
+++ b/Modelfile.Q8_0
@@ -0,0 +1,31 @@
+FROM ./FinSenti-Qwen3-1.7B.Q8_0.gguf
+
+TEMPLATE \"\"\"<|im_start|>system
+You are a financial sentiment analyst. Analyze the given financial text and provide:
+1. Your reasoning in <reasoning> tags
+2. Your sentiment classification (positive, negative, or neutral) in <answer> tags
+
+Always use this exact format:
+<reasoning>
+[Your step-by-step analysis]
+</reasoning>
+<answer>[positive/negative/neutral]</answer><|im_end|>
+<|im_start|>user
+{{ .Prompt }}<|im_end|>
+<|im_start|>assistant
+<think>
+</think>
+
+\"\"\"
+
+PARAMETER stop "<|im_end|>"
+PARAMETER stop "</answer>"
+PARAMETER stop "<|endoftext|>"
+PARAMETER stop "<|im_start|>"
+
+PARAMETER temperature 0.3
+PARAMETER top_p 0.9
+PARAMETER top_k 40
+PARAMETER repeat_penalty 1.15
+PARAMETER num_ctx 1024
+PARAMETER num_predict 512
--- a/README.md
+++ b/README.md
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+---
+license: apache-2.0
+language:
+  - en
+base_model: Ayansk11/FinSenti-Qwen3-1.7B
+datasets:
+  - Ayansk11/FinSenti-Dataset
+pipeline_tag: text-generation
+library_name: gguf
+tags:
+  - finance
+  - financial-sentiment
+  - chain-of-thought
+  - reasoning
+  - gguf
+  - llama-cpp
+  - ollama
+  - quantized
+  - finsenti
+---
+# FinSenti-Qwen3-1.7B - GGUF
+
+GGUF builds of [FinSenti-Qwen3-1.7B](https://huggingface.co/Ayansk11/FinSenti-Qwen3-1.7B)
+for use with [Ollama](https://ollama.com), [llama.cpp](https://github.com/ggerganov/llama.cpp),
+LM Studio, KoboldCpp, and other GGUF-compatible runtimes.
+
+This is the same model as the SafeTensors repo, just converted and
+quantized so you can run it on a CPU or a small GPU without pulling in
+PyTorch.
+
+## Files in this repo
+
+| File | Quant | Size | Notes |
+|------|-------|------|-------|
+| `FinSenti-Qwen3-1.7B.Q4_K_M.gguf` | Q4_K_M | 1.10 GB | Smallest, mild quality dip. Default pick for laptops. |
+| `FinSenti-Qwen3-1.7B.Q5_K_M.gguf` | Q5_K_M | 1.26 GB | Balanced quality and size. |
+| `FinSenti-Qwen3-1.7B.Q8_0.gguf` | Q8_0 | 1.83 GB | Closest to bf16, biggest file. |
+
+If you're not sure which to pick: **start with Q4_K_M**. It's the smallest
+file, it runs everywhere, and the quality drop versus the original bf16
+weights is small for a model this size.
+
+## Quick start (llama.cpp)
+
+```bash
+# Download the Q4_K_M file (or pick a different quant from the table above)
+huggingface-cli download Ayansk11/FinSenti-Qwen3-1.7B-GGUF FinSenti-Qwen3-1.7B.Q4_K_M.gguf --local-dir .
+
+# Run it
+./llama-cli -m FinSenti-Qwen3-1.7B.Q4_K_M.gguf \
+  --system "You are a financial sentiment analyst. For each headline you receive, write a short reasoning chain inside <reasoning>...</reasoning> tags, then give a single label inside <answer>...</answer> tags. The label must be exactly one of: positive, negative, neutral." \
+  -p "Apple beats Q4 estimates as iPhone sales jump 12% year over year." \
+  -n 256
+```
+
+## Quick start (Ollama)
+
+This repo ships a `Modelfile` for each quant. To register the Q4_K_M build
+under the name `finsenti-qwen3-1-7b`:
+
+```bash
+huggingface-cli download Ayansk11/FinSenti-Qwen3-1.7B-GGUF \
+  FinSenti-Qwen3-1.7B.Q4_K_M.gguf Modelfile.Q4_K_M --local-dir ./finsenti-tmp
+cd finsenti-tmp
+ollama create finsenti-qwen3-1-7b -f Modelfile.Q4_K_M
+
+# Then chat with it
+ollama run finsenti-qwen3-1-7b "Apple beats Q4 estimates as iPhone sales jump 12% year over year."
+```
+
+You should see output like:
+
+```
+<reasoning>
+Beating estimates is a positive earnings surprise. A 12% YoY iPhone sales jump in the company's biggest product line points to demand strength. Both signals push the read positive.
+</reasoning>
+<answer>positive</answer>
+```
+
+## Quick start (Python via llama-cpp-python)
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(
+    model_path="./FinSenti-Qwen3-1.7B.Q4_K_M.gguf",
+    n_ctx=2048,
+    n_threads=8,
+)
+
+system = (
+    "You are a financial sentiment analyst. For each headline you receive, "
+    "write a short reasoning chain inside <reasoning>...</reasoning> tags, "
+    "then give a single label inside <answer>...</answer> tags. The label "
+    "must be exactly one of: positive, negative, neutral."
+)
+
+resp = llm.create_chat_completion(
+    messages=[
+        {"role": "system", "content": system},
+        {"role": "user", "content": "Apple beats Q4 estimates as iPhone sales jump 12% year over year."},
+    ],
+    max_tokens=256,
+    temperature=0.0,
+)
+print(resp["choices"][0]["message"]["content"])
+```
+
+## Hardware
+
+The Q4_K_M build is about 1.10 GB on disk and needs
+roughly 2 GB of free RAM at runtime. On a modern laptop
+CPU you should see 15-40 tokens per second depending on the size of the
+model and your core count. Throwing it on a small GPU (Apple Silicon, a
+6-8 GB NVIDIA card) gets you considerably faster generation.
+
+If you need more headroom, the Q5_K_M and Q8_0 files are progressively
+closer to the original bf16 quality at the cost of size.
+
+## Picking a quant
+
+- **Q4_K_M** (1.10 GB): the default for laptops
+  and small servers. Mild quality dip versus full precision but fits
+  almost anywhere.
+- **Q5_K_M** (1.26 GB): a step up if you have
+  the RAM. Most people won't notice the difference from Q8.
+- **Q8_0** (1.83 GB): closest to the bf16 weights.
+  Use this if you want the cleanest output and have the disk space.
+
+## Run it on your phone
+
+This model is small enough to run entirely on-device. The Q4_K_M build is
+1.10 GB on disk and needs roughly 1.8 GB of free RAM
+during inference, so it fits on most phones with 4 GB+ RAM (roughly any
+Android flagship from 2020 onward, or iPhone 11 and newer).
+
+### iOS
+
+The easiest path is [PocketPal AI](https://apps.apple.com/app/id6502579498)
+(free, App Store):
+
+1. Install PocketPal AI from the App Store.
+2. Open the app and go to **Models** -> **+** -> **Add from Hugging Face**.
+3. Search for `Ayansk11/FinSenti-Qwen3-1.7B-GGUF` and select `FinSenti-Qwen3-1.7B.Q4_K_M.gguf`.
+4. Tap download; the file is 1.10 GB.
+5. Once downloaded, tap the model to load it. Open the chat tab.
+6. Set the system prompt (gear icon) to:
+   > You are a financial sentiment analyst. For each headline you receive,
+   > write a short reasoning chain inside `<reasoning>...</reasoning>` tags,
+   > then give a single label inside `<answer>...</answer>` tags. The label
+   > must be exactly one of: positive, negative, neutral.
+7. Send a headline like *"Apple beats Q4 estimates as iPhone sales jump 12% YoY"*
+   and you'll get back the reasoning chain plus the label.
+
+[LLMFarm](https://apps.apple.com/app/id6443968971) and
+[Private LLM](https://privatellm.app/) work too if you already use them.
+
+### Android
+
+PocketPal AI is on
+[Google Play](https://play.google.com/store/apps/details?id=com.pocketpalai)
+as well, with the same flow as the iOS version.
+
+If you'd rather avoid the Play Store,
+[ChatterUI](https://github.com/Vali-98/ChatterUI) is a free, open-source
+client. Install the APK from the GitHub Releases page, then add the model
+from Hugging Face inside the app.
+
+### Tips for phone usage
+
+- **Keep max output tokens around 256.** A reasoning chain plus an answer
+  rarely needs more than that.
+- **Inference is fully offline** once the model is downloaded. No data
+  leaves your phone.
+- **Heat and battery:** one classification finishes in a few seconds, but
+  running hundreds in a loop will warm the device up. Charge while batching.
+- **Stick with Q4_K_M on phones.** The quality difference vs Q5/Q8 for
+  sentiment labels is small, and the smaller file leaves more headroom for
+  the OS.
+
+## Prompt format
+
+Same as the base model. Use the system prompt verbatim, put the headline
+or short snippet in the user turn, and parse the `<answer>...</answer>`
+block for the label.
+
+## Limitations
+
+GGUF is a faithful conversion of the base model, so the same caveats apply:
+
+- English only
+- Short text only (training context was 2048 tokens)
+- Three labels: positive, negative, neutral
+- It explains its read but it isn't doing finance research; don't use the
+  reasoning chain as investment advice
+
+Quantization adds a small extra error on top of the base model. For
+Q4_K_M on a model this size you'll see occasional disagreement with the
+bf16 model on borderline headlines, usually neutral-vs-positive flips.
+
+## Related FinSenti models
+
+Other sizes and bases trained with the same recipe:
+
+- **Qwen3**: [Qwen3-0.6B](https://huggingface.co/Ayansk11/FinSenti-Qwen3-0.6B), [Qwen3-4B](https://huggingface.co/Ayansk11/FinSenti-Qwen3-4B), [Qwen3-8B](https://huggingface.co/Ayansk11/FinSenti-Qwen3-8B)
+- **Qwen3.5**: [Qwen3.5-0.8B](https://huggingface.co/Ayansk11/FinSenti-Qwen3.5-0.8B), [Qwen3.5-2B](https://huggingface.co/Ayansk11/FinSenti-Qwen3.5-2B), [Qwen3.5-4B](https://huggingface.co/Ayansk11/FinSenti-Qwen3.5-4B), [Qwen3.5-9B](https://huggingface.co/Ayansk11/FinSenti-Qwen3.5-9B)
+- **DeepSeek**: [DeepSeek-R1-1.5B](https://huggingface.co/Ayansk11/FinSenti-DeepSeek-R1-1.5B)
+
+The full-precision SafeTensors version of this model is at
+[Ayansk11/FinSenti-Qwen3-1.7B](https://huggingface.co/Ayansk11/FinSenti-Qwen3-1.7B), and the
+training data is at
+[Ayansk11/FinSenti-Dataset](https://huggingface.co/datasets/Ayansk11/FinSenti-Dataset).
+
+## Citation
+
+```bibtex
+@misc{shaikh2026finsenti,
+  title  = {FinSenti: Small Language Models for Financial Sentiment with Chain-of-Thought Reasoning},
+  author = {Shaikh, Ayan},
+  year   = {2026},
+  url    = {https://huggingface.co/collections/Ayansk11/finsenti},
+  note   = {Indiana University}
+}
+```
+
+## License
+
+Apache 2.0.