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Model: dhanushmekaka/qwen25-1.5b-invoice-extraction-v3 Source: Original Platform
2026-09-11 06:58:17 +08:00
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--- a/README.md
+++ b/README.md
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+
+
+# qwen25-1.5b-invoice-extraction-v3
+
+A QLoRA fine-tune of **Qwen2.5-1.5B-Instruct** that extracts structured financial terms from SEC
+contract / commitment-letter text and returns them as a fixed-schema JSON object.
+
+## Model Description
+
+- **Base model:** `Qwen/Qwen2.5-1.5B-Instruct`
+- **Fine-tuning method:** QLoRA (4-bit NF4), LoRA r=64, alpha=64, applied to all attention and MLP
+  projection layers, trained with [Unsloth](https://github.com/unslothai/unsloth)
+- **Task:** Given a contract excerpt, extract every financial value present and label it with a
+  `term_type` from a fixed vocabulary, returned as strict JSON.
+- **Trained on:** `TheTokenFactory/sec-contracts-financial-extraction-instructions` (3,430 examples),
+  using a two-stage curriculum (core financial fields first, then the full dataset).
+
+## Output Format
+
+```json
+{
+  "financial_values": [
+    {"value": "US$3,050,000,000", "term_type": "credit_facility"},
+    {"value": "8.5%", "term_type": "interest_rate"}
+  ]
+}
+```
+
+`term_type` is restricted to: `interest_rate`, `equity_grant`, `bonus`, `credit_facility`,
+`salary`, `threshold`, `fee`, `loan_amount`, `compensation`, `retirement_benefit`, `other`,
+`purchase_price`, `severance`.
+
+## Intended Use
+
+Drafting / first-pass extraction of financial terms from contracts, commitment letters, and
+similar legal-financial documents, as an aid for a human reviewer — **not** as a standalone
+source of truth for financial or legal decisions.
+
+## How to Use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "dhanushmekaka/qwen25-1.5b-invoice-extraction-v3"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+messages = [
+    {"role": "system", "content": "You are a strict legal extraction AI... Output strictly as JSON."},
+    {"role": "user", "content": "<contract excerpt here>"},
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=512, do_sample=False)
+print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+## Evaluation
+
+Measured on a 50-example held-out split of the training dataset:
+
+| Metric | Score |
+|---|---|
+| Exact Match Accuracy (full JSON object) | 16.0% |
+| JSON Output Validity | 94.0% |
+| Field-Level Extraction Accuracy | 44.9% |
+| Avg. Inference Latency | 7.07 s |
+| Avg. Throughput | 17.4 tok/s |
+
+| `term_type` | Gold count | Precision | Recall |
+|---|---|---|---|
+| bonus | 9 | 66.7% | 44.4% |
+| compensation | 4 | 0.0% | 0.0% |
+| credit_facility | 1 | n/a | 0.0% |
+| equity_grant | 3 | 40.0% | 66.7% |
+| fee | 7 | 100.0% | 42.9% |
+| interest_rate | 6 | 75.0% | 50.0% |
+| loan_amount | 2 | 66.7% | 100.0% |
+| purchase_price | 2 | 100.0% | 50.0% |
+| retirement_benefit | 1 | n/a | 0.0% |
+| salary | 7 | 50.0% | 57.1% |
+| threshold | 7 | 42.9% | 42.9% |
+
+## Limitations
+
+This is a **v3, still-converging checkpoint** — training was stopped by a wall-clock budget at
+90.8% of the planned steps (epoch 5.45 of 6), with eval loss still decreasing. The numbers above
+should be read as a snapshot of an in-progress model, not a final result:
+
+- **Exact Match (16%)** is a strict all-or-nothing metric over the whole JSON object; a single
+  wrong value anywhere in a multi-field extraction counts as a full miss. Field-Level Accuracy
+  (44.9%) is a better read on per-value correctness.
+- **Rare `term_type`s** (`retirement_benefit`, `purchase_price`, `severance`, `compensation`,
+  `other`) have limited training coverage (under 250 combined examples vs. 850 for
+  `interest_rate` alone), and their precision/recall above is computed on single-digit eval
+  counts — treat those figures as directional, not reliable.
+- **JSON validity (94%)** means roughly 1 in 17 outputs needs a parsing fallback or retry in
+  production.
+- The 50-example eval set is small; metrics may shift with a larger held-out set.
+
+**Recommended before production use:** complete the remaining training epoch, expand the eval set,
+and add human review for `term_type`s with fewer than ~100 training examples.
+
+## Training Data
+
+`TheTokenFactory/sec-contracts-financial-extraction-instructions` — SEC contract excerpts
+(commitment letters, credit agreements, employment/compensation terms) paired with gold JSON
+financial-term annotations.
+
+## License
+
+Apache 2.0 (inherits from the Qwen2.5-1.5B-Instruct base model — verify against the base model's
+license terms before commercial use).---
+base_model: unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen2
+license: apache-2.0
+language:
+- en
+---
+
+# Uploaded finetuned  model
+
+- **Developed by:** dhanushmekaka
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit
+
+This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
--- a/added_tokens.json
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--- a/chat_template.jinja
+++ b/chat_template.jinja
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+{%- if tools %}
+    {{- '<|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 %}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,59 @@
+{
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
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+        "full_attention",
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+        "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"
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+    "max_position_embeddings": 32768,
+    "max_window_layers": 21,
+    "model_type": "qwen2",
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+    "rms_norm_eps": 1e-06,
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+    "rope_theta": 1000000.0,
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+    "tie_word_embeddings": true,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.7.2",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}
--- a/generation_config.json
+++ b/generation_config.json
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+{
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+  "eos_token_id": [
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+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.56.2"
+}
--- a/merges.txt
+++ b/merges.txt
--- a/model.safetensors
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--- a/special_tokens_map.json
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--- a/tokenizer.json
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+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "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"
+}
--- a/vocab.json
+++ b/vocab.json