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 *.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

@@ -1,3 +1,223 @@
 ---
 license: apache-2.0
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+library_name: peft
+tags:
+  - argument-mining
+  - fact-checking
+  - lora
+  - qwen
+  - distillation
+language: [en]
+pipeline_tag: text-generation
 ---
+
+# ArgParser-v4
+
+A Qwen-1.5B LoRA that extracts argument structure — claims, premises,
+citations, and support/attack relations — from political claims and
+argumentative prose. This is the fourth iteration of a distillation
+project, and the one that actually works well enough to plug into a
+real fact-checking pipeline.
+
+## Where this came from
+
+I built an argument-aware retrieval pipeline for Politifact-style
+fact-checking. Given a claim like "Politician X said Y," it retrieves
+evidence using argument-role-targeted queries and returns a 6-way truth
+verdict. When the parser was reading gold argument annotations from the
+LIARArg dataset, this pipeline hit 0.422 6-way F1 versus 0.114 for a
+flat-RAG baseline. The 0.308 gap was the whole reason to build it.
+
+But that gap only means something if a real parser can slot in at
+inference time. Gold annotations aren't available for actual incoming
+claims. So the question became: what parser closes that gap?
+
+First cut, Phase 2-α, was `gpt-oss-120b` running zero-shot on Cerebras.
+It closed about 45% of the gap (integration F1 0.254). Real, useful,
+but calling a 120B cloud model per claim isn't practical for anything
+resembling deployment.
+
+Phase 2-β was the distillation project. Four iterations. Can a small
+local model preserve most of the gain?
+
+## The four iterations
+
+**v1** was Qwen-0.5B, full fine-tune on four argument-mining corpora
+(AbstRCT, Microtext, CDCP, PERSPECTRUM), 1,494 records, 3 epochs. The
+smallest reasonable baseline. In-domain comp-F1 averaged 0.108 across
+the four held-out test sets. High empty rates on some domains
+(PERSPECTRUM: 91% empty). The point was to get the pipeline plumbing
+right, not to publish a number.
+
+**v2** kept the same data but moved to Qwen-1.5B with LoRA r=16
+(α=32, dropout 0.05, target `q_proj,k_proj,v_proj,o_proj`). 3 epochs.
+In-domain comp-F1: 0.219, roughly double v1. Microtext premise F1
+jumped from 0.000 to 0.680. AbstRCT empty rate 75% → 50%. Scale +
+LoRA + longer training context are the dominant levers here.
+
+**v3** was v2's adapter continued for one more epoch after adding a
+fifth corpus (AAEC, 402 persuasive essays). Marginal in-domain
+improvement (0.229). PERSPECTRUM actually regressed slightly, which
+was the first sign that adding more of the same kind of extractive
+gold hits diminishing returns quickly. Then I tried v3 on the actual
+LIARArg parse and hit 83% empty rate on the first 64 rows. Killed
+that run. The lesson was clear: extractive gold from academic
+argument-mining corpora doesn't teach a small student to handle
+Politifact-style claims. The distribution gap is too wide.
+
+**v4** is this model. Two changes from v3:
+
+1. Fresh adapter, not continual. Clean A/B against v3.
+2. I generated 2,123 silver labels on LIARArg train articles using
+   `gpt-oss-120b` via Cerebras. Both the extracted argument structure
+   and the model's Chain-of-Thought reasoning came back (the CoT was
+   in `message.reasoning`, which I captured almost by accident). The
+   training code's target-formatter passes reasoning through as
+   `<think>...</think>{json}` when present, so v4 accidentally
+   became CoT-aware for LIARArg-style inputs — while staying purely
+   extractive on the gold in-domain corpora (where reasoning was empty).
+
+This wasn't planned. It just happened, and it turned out to be exactly
+what was missing.
+
+Training details: 3,617 records total, 3 epochs, fresh LoRA adapter,
+fp16, Adafactor, gradient checkpointing. Batch 1, grad accum 32.
+About 29 hours on a single GTX 1080 Ti.
+
+## What v4 actually does
+
+The load-bearing number is Phase 1 integration on LIARArg — the whole
+reason to build any of this:
+
+| Metric | v4 | Phase 2-α teacher (120B) | Teacher retention |
+|---|---|---|---|
+| 6-way F1 | 0.217 | 0.254 | 85% |
+| 3-way F1 | 0.457 | 0.461 | 99% |
+| within-1 accuracy | 0.605 | 0.616 | 98% |
+
+Flat-RAG baseline for reference: 0.114. v4 beats it by 0.103 and
+closes 33% of the gold-parser gap using a locally-runnable 1.5B model.
+
+LIARArg parse empty rate went from v3's 83% down to 23%. Silver + CoT
+was the missing piece.
+
+In-domain retention is modest — 0.192 comp-F1 averaged across the five
+training corpora, slightly regressed from v3's 0.229. That's the cost
+of a fresh 3-epoch run versus v3's effective 4 epochs. The trade was
+worth it for the cross-domain transfer.
+
+## OOD probes
+
+To characterize where v4 stops transferring, I ran three unseen-domain
+probes:
+
+**AMPERSAND** (Chakrabarty et al. 2019, Reddit ChangeMyView). Binary
+is-argumentative F1 = 0.819 with recall 0.970 on 150 balanced
+sentences. v4 catches essentially all argumentative content in Reddit
+debate. The 30% false-positive rate comes from over-flagging fragments
+and borderline sentences; some of those are arguably right and just
+disagree with the annotator.
+
+**PERSUADE 2.0** (Kaggle Feedback Prize, student argumentative essays).
+Component F1 macro = 0.193 with 45.7% extraction rate on 25 essays.
+Claim F1 = 0.351, premise F1 = 0.034. v4 finds about half of
+PERSUADE's argumentative spans and labels claim-like content
+reasonably. Premise F1 collapses because PERSUADE's Evidence and
+Rebuttal categories are much narrower than v4's premise concept.
+
+**ECHR** (European Court of Human Rights case briefs). Component F1
+= 0.074 with 9.7% extraction rate against a proxy gold derived from
+ECHR's agent labels. Legal reasoning is structurally distant from
+anything v4 saw in training.
+
+Ordering (Reddit > essays > legal) tracks discourse-register proximity
+to v4's training data. Predictable, but useful to have quantified.
+
+## Usage
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch, re, json
+
+base_id    = "Qwen/Qwen2.5-1.5B-Instruct"
+adapter_id = "properexit/ArgParser-v4"
+
+tok = AutoTokenizer.from_pretrained(base_id)
+base = AutoModelForCausalLM.from_pretrained(
+    base_id, torch_dtype=torch.float16, device_map="auto"
+)
+model = PeftModel.from_pretrained(base, adapter_id)
+
+INSTR = ("Extract all argument components and relations from the text. "
+         "Output strict JSON with claim_components, premise_components, "
+         "citation_components, and relations.")
+
+def parse(text, max_new_tokens=1024):
+    prompt = tok.apply_chat_template(
+        [{"role": "user", "content": f"{INSTR}\n\nTEXT:\n{text}"}],
+        tokenize=False, add_generation_prompt=True,
+    )
+    enc = tok(prompt, return_tensors="pt", truncation=True,
+              max_length=2048).to(model.device)
+    out = model.generate(**enc, max_new_tokens=max_new_tokens,
+                         do_sample=False,
+                         pad_token_id=tok.pad_token_id,
+                         eos_token_id=tok.eos_token_id)
+    raw = tok.decode(out[0, enc["input_ids"].shape[-1]:],
+                     skip_special_tokens=True)
+    cleaned = re.sub(r"<think>.*?</think>", "", raw, flags=re.DOTALL)
+    m = re.search(r"\{.*\}", cleaned, flags=re.DOTALL)
+    return (json.loads(m.group()) if m else None), raw
+
+pred, raw = parse(
+    "The Obama administration is putting Border Patrol agents in a chokehold."
+)
+print(pred)
+```
+
+## Things worth knowing before using this
+
+The model emits `<think>...</think>` blocks on political and
+opinionated inputs (that's where it saw CoT during training). On
+formal or structured text — legal writing, some scientific abstracts
+— it goes straight to JSON. Neither is a bug, it just needs handling
+if you're stripping the raw generation.
+
+Long inputs can trigger a relation-generation loop that leaves the JSON
+unclosed. What happens: the model emits valid `claim_components` and
+`premise_components` early, then gets stuck emitting
+`{"src": N, "tgt": M, "type": "support"}` tuples in a repeating
+pattern until it hits `max_new_tokens`. You never see the closing `}`
+so strict JSON parsing rejects everything. The workaround is either
+setting `max_new_tokens` conservatively, or using a lenient parser
+that pulls each valid `{...}` object out of each section
+independently and dedupes relations. I use the second approach in
+the training repo.
+
+v4 systematically over-predicts spans on OOD text — precision runs
+lower than recall in every probe. On borderline sentences it defaults
+to labeling as claim. Something to keep in mind if downstream
+consumers care about precision.
+
+Fine-grained annotation schemas map imperfectly to v4's binary
+claim/premise split. PERSUADE's Evidence/Rebuttal distinction and
+ECHR's Court/Applicant/State agent labels don't translate directly.
+v4 knows a claim from a premise; it doesn't know PERSUADE's Evidence
+from PERSUADE's Rebuttal.
+
+## Training summary
+
+- Base: `Qwen/Qwen2.5-1.5B-Instruct`
+- LoRA: r=16, α=32, dropout 0.05, on `q_proj,k_proj,v_proj,o_proj`
+- Training records: 3,617 (5 gold corpora + 2,123 LIARArg silver)
+- Epochs: 3, fresh adapter
+- Optimizer: Adafactor, fp16, gradient checkpointing
+- Hardware: single NVIDIA GTX 1080 Ti
+- Wall clock: ~29 h
+
+## License
+
+Apache 2.0. Base model (Qwen 2.5) is also Apache 2.0. Use however you
+want, no warranty.

adapter_config.json

@@ -0,0 +1,45 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct",
+  "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": 0.05,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "o_proj",
+    "v_proj",
+    "k_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+oid sha256:030161fc03794c4aaabde5a20e82ec19926e3b9aea8df6468251b36f1f7fc9e9
+size 17462432

chat_template.jinja

@@ -0,0 +1,54 @@
+{%- 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 %}

checkpoint-248/README.md

@@ -0,0 +1,207 @@
+---
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.19.1

checkpoint-248/adapter_config.json

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+  "base_model_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct",
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+  "ensure_weight_tying": false,
+  "eva_config": null,
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+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
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+    "v_proj",
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+  "task_type": "CAUSAL_LM",
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+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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checkpoint-248/trainer_state.json

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+---
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.19.1

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