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Model: sag-uniroma2/FrameLLaMA-3.1-8B-Instruct-FullFN17
Source: Original Platform

.gitattributes

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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- trl
+license: apache-2.0
+language:
+- en
+---
+
+# Uploaded  model
+
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
+
+This llama 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)
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+from unsloth import is_bfloat16_supported
+
+# Precision
+dtype = torch.bfloat16 if is_bfloat16_supported() else torch.float16
+
+# Models
+BASE_MODEL = "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit"
+LORA_MODEL = "sag-uniroma2/FrameLLaMA-3.1-8B-Instruct-FullFN17"
+
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    torch_dtype=dtype,
+    device_map="auto"
+)
+
+# Load LoRA
+model = PeftModel.from_pretrained(base_model, LORA_MODEL)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(LORA_MODEL, use_fast=True)
+
+# Device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+# 🔥 ===== YOUR SAMPLE HERE =====
+premise = "John drowned Martha."
+hypothesis = "Martha died."
+
+# Prompt
+input_text = f"""
+        Judge if the hypothesis necessarily follows from the premise.
+        Consider the truth value of the premise. If the premise is true, does it necessarily mean that the hypothesis must also be true?
+        Output E if the hypothesis must always be true.
+        Output C if the hypothesis must always be false.
+        Output N if the hypothesis may be either true or false.
+        Do not output anything other than letters E, C, or N.
+
+        Premise: {premise}
+        Hypothesis: {hypothesis}
+        # Output:"""
+
+# Tokenize
+inputs = tokenizer(input_text, return_tensors="pt").to(device)
+
+# Generate
+with torch.no_grad():
+    output_ids = model.generate(
+        **inputs,
+        max_new_tokens=10,
+        do_sample=False
+    )
+
+# Decode only generated part
+input_len = inputs["input_ids"].shape[1]
+generated = output_ids[0][input_len:]
+response = tokenizer.decode(generated, skip_special_tokens=True).strip()
+
+# Print result
+print("Premise:", premise)
+print("Hypothesis:", hypothesis)
+print("Prediction:", response)
+```
+## Description
+
+**FrameLLaMA-3.1-8B-Instruct-FullFN17** is a frame-aware language model designed to improve event-level semantic reasoning in Large Language Models (LLMs). The model injects structured knowledge from FrameNet 1.7 into Llama-3.1-8B-Instruct using parameter-efficient LoRA fine-tuning. 
+
+Unlike standard instruction tuning, this model leverages **principle-oriented supervision**, where frame definitions, participant roles, semantic types, lexical senses, and frame-to-frame relations are converted into structured question–answer tasks. This enables the model to learn reusable semantic constraints rather than isolated facts.
+
+The model is optimized for tasks where meaning depends on **event structure, participant roles, and lexical disambiguation**, such as Natural Language Inference (NLI) and Semantic Role Labeling (SRL).
+
+---
+
+## Model Details
+
+- **Base Model**: Llama-3.1-8B-Instruct  
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)  
+- **Training Data**: FrameNet 1.7 (full inventory, 1,200+ frames)  
+- **Supervision Type**: Principle-oriented QA-style prompts  
+- **Tasks**: NLI, SRL (evaluation), semantic reasoning  
+- **Model Type**: Instruction-tuned causal language model  
+
+---
+
+## Key Features
+
+- ✅ **Full FrameNet Coverage**: Trained on 1,200+ frames.  
+- ✅ **Principle-Oriented Learning**: Encodes role constraints, semantic types, and frame relations  
+- ✅ **Event-Level Reasoning**: Improves understanding of causality, entailment, and contradiction  
+- ✅ **Frame-Aware Inference**: Better handling of lexical ambiguity and role compatibility  
+- ✅ **Parameter-Efficient Training**: Uses LoRA for scalable adaptation  
+- ✅ **Generalization Beyond SRL**: Transfers to NLI and semantic inference tasks  
+
+---
+
+## Performance
+
+- Evaluated on:
+  - **SNLI (diagnostic subset)** for event-level inference  
+  - **CONLL-style FrameNet SRL dataset** (via OpenSesame preprocessing)  
+
+- Observed improvements:
+  - Strong gains in **entailment and contradiction detection**  
+  - Improved **frame identification and role-span alignment** in SRL  
+  - Reduced reliance on surface-level lexical cues  
+
+---
+
+## Use Cases
+
+- **Natural Language Inference (NLI)**: Event-based reasoning and entailment detection  
+- **Semantic Role Labeling (SRL)**: Frame and role prediction  
+- **Event Understanding**: Modeling causality and participant structure  
+- **Linguistically-Informed AI**: Applications requiring structured semantic interpretation  
+- **Research on LLM Interpretability**: Studying structured knowledge injection  
+
+---
+
+## Output Format
+
+- Single token or short response:
+  - **E** → Entailment  
+  - **C** → Contradiction  
+  - **N** → Neutral  
+
+- Outputs are concise and reflect event-level semantic reasoning.
+
+---
+
+## Training Details
+
+- FrameNet structures (definitions, roles, relations) are **linearized into QA-style templates**
+- Supervision includes:
+  - Frame definitions  
+  - Role constraints  
+  - Semantic types  
+  - Lexical unit disambiguation  
+  - Frame-to-frame relations  
+- Negative samples generated via similarity-based filtering  
+- Fine-tuned using LoRA for efficiency and scalability  
+
+---
+
+## GitHub
+
+For training scripts, datasets, and evaluation:
+
+👉 https://github.com/crux82/FrameLLaMA
+
+## Citation
+
+If you use this model, please cite:
+
+
+

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": {
+    "base_model_class": "LlamaForCausalLM",
+    "parent_library": "transformers.models.llama.modeling_llama",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "unsloth/meta-llama-3.1-8b-instruct-bnb-4bit",
+  "bias": "none",
+  "corda_config": null,
+  "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": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "o_proj",
+    "up_proj",
+    "k_proj",
+    "q_proj",
+    "gate_proj",
+    "v_proj",
+    "down_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

chat_template.jinja

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+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- set date_string = "26 Jul 2024" %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message + builtin tools #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if builtin_tools is defined or tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{%- if builtin_tools is defined %}
+    {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- "<|python_tag|>" + tool_call.name + ".call(" }}
+            {%- for arg_name, arg_val in tool_call.arguments | items %}
+                {{- arg_name + '="' + arg_val + '"' }}
+                {%- if not loop.last %}
+                    {{- ", " }}
+                {%- endif %}
+                {%- endfor %}
+            {{- ")" }}
+        {%- else  %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+            {{- '{"name": "' + tool_call.name + '", ' }}
+            {{- '"parameters": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- "}" }}
+        {%- endif %}
+        {%- if builtin_tools is defined %}
+            {#- This means we're in ipython mode #}
+            {{- "<|eom_id|>" }}
+        {%- else %}
+            {{- "<|eot_id|>" }}
+        {%- endif %}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "float16",
+  "eos_token_id": 128009,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.56.2",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.10.9",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "max_length": 131072,
+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.56.2"
+}

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