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@@ -33,3 +33,4 @@ saved_model/**/* 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

README.md

@@ -0,0 +1,158 @@
+---
+base_model: unsloth/qwen2.5-3b-instruct-bnb-4bit
+library_name: peft
+model_name: job-posting-extractor-qwen
+tags:
+- base_model:adapter:unsloth/qwen2.5-3b-instruct-bnb-4bit
+- lora
+- sft
+- transformers
+- unsloth
+- json-extraction
+- web-scraping
+- job-postings
+license: cc-by-4.0
+pipeline_tag: text-generation
+datasets:
+- HelixCipher/job-training-data
+---
+
+# Job Posting Extractor (Qwen2.5-3B)
+
+A fine-tuned version of Qwen2.5-3B-Instruct specialized in extracting structured JSON data from job postings. Built to replace expensive API calls for web scraping tasks.
+
+## What This Model Does
+
+Given a job posting in markdown format, this model extracts structured JSON containing:
+- job_title
+
+- company
+
+- location
+
+- description
+
+- salary (when available)
+
+- requirements (when available)
+
+## Quick Start
+
+```python
+from unsloth import FastLanguageModel
+import json
+
+# Load model from HuggingFace
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="HelixCipher/job-posting-extractor-qwen",
+    max_seq_length=2048,
+    load_in_4bit=True,
+)
+FastLanguageModel.for_inference(model)
+
+# Example input
+job_markdown = """# Job Position
+**Position:** Senior Python Developer
+**Company:** TechCorp
+**Location:** San Francisco, CA
+
+## Job Description
+We are looking for an experienced Python developer...
+"""
+
+# Extract JSON
+messages = [
+    {"role": "system", "content": "You are a JSON extraction assistant. Always output ONLY valid JSON."},
+    {"role": "user", "content": f"Extract job fields as JSON.\n\n{job_markdown}"}
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens=500, temperature=0.1)
+
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print(result)
+```
+
+## Training Details
+
+- **Base Model**: unsloth/qwen2.5-3b-instruct-bnb-4bit.
+
+- **Training Data**: 12,000 job posting examples.
+
+- **Training Approach**: LoRA with Unsloth.
+
+- **Fine-tuning Library**: TRL (Transformer Reinforcement Learning).
+
+### Framework Versions
+
+- PEFT: 0.18.1
+
+- TRL: 0.24.0
+
+- Transformers: 4.57.6
+
+- PyTorch: 2.10.0+cu126
+
+## Use Cases
+
+- Extract job postings from scraped websites.
+
+- Convert unstructured job listings to structured JSON.
+
+- Automate data collection for job aggregators.
+
+- Replace expensive LLM API calls with local inference.
+
+## Limitations
+
+- Trained specifically on job postings - may not work well for other data types.
+
+- Works best with markdown-formatted input (similar to html2text output).
+
+- Maximum context: 2048 tokens
+
+## License & Attribution
+
+This project is licensed under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.
+
+You are free to **use, share, copy, modify, and redistribute** this material for any purpose (including commercial use), **provided that proper attribution is given**.
+
+### Attribution requirements
+
+Any reuse, redistribution, or derivative work **must** include:
+
+1. **The creator's name**: `HelixCipher`
+
+2. **A link to the original repository**:  
+
+   https://github.com/HelixCipher/fine-tuning-an-local-llm-for-web-scraping
+
+3. **An indication of whether changes were made**
+
+4. **A reference to the license (CC BY 4.0)**
+
+#### Example Attribution
+
+> This work is based on *Fine-Tuning An Local LLM for Web Scraping* by `HelixCipher`.  
+> Original source: https://github.com/HelixCipher/fine-tuning-an-local-llm-for-web-scraping
+
+> Licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0).
+
+You may place this attribution in a README, documentation, credits section, or other visible location appropriate to the medium.
+
+Full license text: https://creativecommons.org/licenses/by/4.0/
+
+## Citation
+
+```bibtex
+@software{job_posting_extractor,
+  author = {HelixCipher},
+  title = {Job Posting Extractor - Qwen2.5-3B Fine-tuned Model},
+  year = {2026},
+  url = {https://huggingface.co/HelixCipher/job-posting-extractor-qwen}
+}
+```

adapter_config.json

@@ -0,0 +1,50 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Qwen2ForCausalLM",
+    "parent_library": "transformers.models.qwen2.modeling_qwen2",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "unsloth/qwen2.5-3b-instruct-bnb-4bit",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "k_proj",
+    "up_proj",
+    "gate_proj",
+    "o_proj",
+    "down_proj",
+    "q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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added_tokens.json

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+}

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 %}

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
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+  }
+}

tokenizer.json

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tokenizer_config.json

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+}

training_args.bin

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