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Model: Gadsdencode/Nomadic-ICDU-v8
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README.md

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+---
+license: mit
+base_model: mistralai/Mistral-7B-Instruct-v0.3
+base_model_relation: finetune
+pipeline_tag: text-generation
+library_name: transformers
+language:
+  - en
+tags:
+  - mistral
+  - icdu
+  - instruction-tuning
+  - conversational
+  - text-generation
+  - gguf
+---
+
+# Nomadic-ICDU-v8
+
+Nomadic-ICDU-v8 is a 7.25B-parameter, instruction-tuned language model derived from
+[Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3).
+It is designed to demonstrate the ICDU approach: representing a task as an explicit,
+testable unit of intent so that a model can be tuned and evaluated against the
+purpose, principles, constraints, decision rules, and expected behavior of a specific
+workflow.
+
+The repository contains a merged FP16 Transformers checkpoint and GGUF exports for
+local inference.
+
+> **Disclosure status:** The base model, architecture, context-window declaration,
+> checkpoint formats, and prompt template are verifiable from the published files.
+> The public repository does not yet include a reproducible v8 training manifest,
+> dataset manifest, or benchmark report. Where those records are unavailable, this
+> card says so explicitly and does not infer or invent details.
+
+## Model details
+
+| Field | Value |
+|---|---|
+| Model | `Gadsdencode/Nomadic-ICDU-v8` |
+| Model type | Decoder-only causal language model |
+| Architecture | `MistralForCausalLM` |
+| Parameters | 7,248,023,552 |
+| Base model | `mistralai/Mistral-7B-Instruct-v0.3` |
+| Layers / hidden size | 32 / 4,096 |
+| Attention heads / KV heads | 32 / 8 |
+| Vocabulary | 32,768 tokens |
+| Declared maximum positions | 32,768 tokens |
+| Published precision | FP16 |
+| Published local formats | GGUF F16 and GGUF Q4_K_M |
+| Primary language | English |
+| Repository license declaration | MIT |
+| Public checkpoint commit | `a328cf13db7a10a200cf616c2f35a0035a6b637c` |
+| Repository last updated | 2025-08-28 |
+
+Users are responsible for reviewing the upstream base-model terms, the repository
+license, and any obligations that apply to their deployment or data.
+
+## What ICDU-v8 was trained to do
+
+ICDU-v8 is intended to improve task-specific behavior by making the operating
+contract of a workflow explicit. Depending on the ICDU used for a task, that contract
+can include:
+
+- the user's or organization's intent;
+- governing principles and priorities;
+- role, audience, tone, and communication requirements;
+- domain context and input structure;
+- constraints, boundaries, and prohibited actions;
+- decision, escalation, and uncertainty-handling rules; and
+- examples of preferred and non-preferred behavior.
+
+The intended result is a model that follows a defined operating envelope more
+consistently than a generic instruction model, including when a request is
+paraphrased, partially specified, or placed under conflicting pressure.
+
+This is an intended capability, not a guarantee. Users should measure it on their own
+ICDUs, inputs, failure modes, and deployment conditions.
+
+## Base model and training method
+
+The checkpoint configuration identifies
+`mistralai/Mistral-7B-Instruct-v0.3` as the base model. The published repository is a
+merged inference checkpoint, not a standalone adapter.
+
+ICDU project documentation describes the following training and release methodology:
+
+1. Convert the target workflow into structured ICDU training records.
+2. Perform supervised fine-tuning with QLoRA.
+3. Apply preference tuning, including DPO, where preferred and rejected responses are
+   available.
+4. Stress-test behavior with perturbations to role, tone, constraints, inputs, and
+   channel.
+5. Evaluate against task-specific gates using an AI Judge and, where appropriate,
+   human-in-the-loop grading.
+6. Release only after the model satisfies the selected operating thresholds.
+
+The public v8 repository does **not** currently include the run configuration needed
+to prove which of these stages, hyperparameters, adapter settings, random seeds,
+checkpoints, or release gates were used for this exact build. Accordingly, the
+sequence above documents the ICDU project method rather than a reproducible v8
+training log.
+
+## Training data
+
+ICDU training records are designed to encode task intent and expected behavior rather
+than rely only on broad, unstructured instruction examples. A record may contain:
+
+- an intent or objective;
+- principles and prioritization rules;
+- persona, audience, tone, or channel requirements;
+- relevant context and constraints;
+- task inputs;
+- an expected or preferred response;
+- a rejected response or failure example for preference tuning; and
+- escalation or abstention behavior.
+
+### Data disclosure and exclusions
+
+The following v8-specific information is not present in the public repository:
+
+| Disclosure item | Public status |
+|---|---|
+| Number of training, validation, and test examples | Not published |
+| Dataset names and source provenance | Not published |
+| Human-authored versus synthetic-data composition | Not published |
+| Domain and language distribution | Not published |
+| Deduplication and contamination checks | Not published |
+| Copyright and license review | Not published |
+| PII or sensitive-data screening | Not published |
+| Explicit exclusion list | Not published |
+| Preference-pair construction and review process | Not published |
+
+No specific exclusion—such as personal data, customer data, copyrighted material,
+medical records, security-sensitive data, or benchmark test sets—should be assumed
+without a training-data manifest. Deployments that require documented provenance,
+consent, data residency, or regulated-data controls should not rely on this checkpoint
+until the relevant records have been reviewed.
+
+## Required prompt and chat format
+
+Use the tokenizer's bundled Mistral chat template whenever possible. Messages must
+alternate between `user` and `assistant`; an optional `system` message may appear
+first.
+
+### Transformers chat template
+
+```python
+from transformers import AutoTokenizer
+
+model_id = "Gadsdencode/Nomadic-ICDU-v8"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+messages = [
+    {
+        "role": "system",
+        "content": (
+            "Follow the supplied ICDU. Respect its intent, principles, "
+            "constraints, and escalation rules."
+        ),
+    },
+    {
+        "role": "user",
+        "content": "Summarize the case and identify any required escalation.",
+    },
+]
+
+prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+print(prompt)
+```
+
+### Raw single-turn format
+
+```text
+<s>[INST] {optional system instruction}
+
+{user message} [/INST]
+```
+
+### Raw multi-turn format
+
+```text
+<s>[INST] {system instruction}
+
+{first user message} [/INST] {first assistant response}</s>[INST] {next user message} [/INST]
+```
+
+Do not substitute an unrelated chat template. Prompt-template mismatches can cause
+lower-quality responses, leaked control tokens, or unstable turn-taking.
+
+The tokenizer also contains formatting for tool calls and tool results. The presence
+of those tags does not establish reliable tool selection, argument generation, or
+safe autonomous tool use; evaluate tool behavior separately before enabling it.
+
+## Intended uses
+
+Appropriate uses include:
+
+- research and evaluation of ICDU-based task specialization;
+- local or private task-specific assistants;
+- workflow prototypes with explicit intent, constraints, and escalation rules;
+- drafting, classification, summarization, and decision support within a tested
+  operating envelope;
+- policy-sensitive or regulated-workflow support with qualified human review;
+- perturbation testing and comparison against a base instruction model; and
+- creating organization- or domain-specific derivatives using authorized data.
+
+The model is best treated as a component inside a governed workflow, not as an
+independent authority.
+
+## Prohibited uses
+
+Do not use ICDU-v8 for:
+
+- autonomous medical, legal, financial, employment, insurance, credit, housing, or
+  other high-impact decisions;
+- emergency response or safety-critical control without qualified human authority;
+- unsupervised actions that can spend money, modify production systems, disclose
+  data, or create irreversible effects;
+- unlawful surveillance, discrimination, impersonation, fraud, deception, or
+  harassment;
+- generation or operational assistance for weapons, malware, credential theft, or
+  other harmful activity;
+- processing secrets, credentials, personal data, or regulated records without
+  appropriate authorization and technical controls; or
+- representing model output as guaranteed accurate, compliant, unbiased, or
+  professionally approved.
+
+These use restrictions state the maintainer's intended operating policy. They do not
+replace applicable law, professional obligations, or license terms.
+
+## Known limitations
+
+- **No published v8 benchmark report.** Task-fit and safety claims have not yet been
+  substantiated by reproducible public results.
+- **Incomplete training-data disclosure.** Dataset scale, sources, composition,
+  contamination testing, and exclusions are not currently documented.
+- **Hallucinations.** The model can produce fluent but false, unsupported, or
+  internally inconsistent content.
+- **Prompt sensitivity.** Small changes in instructions, chat formatting, context
+  order, or generation settings can change behavior.
+- **Base-model limits.** This is a 7B-class model and should not be expected to match
+  larger frontier systems on broad knowledge, complex reasoning, coding, or
+  multilingual tasks.
+- **Language coverage.** English is the declared primary language. Quality in other
+  languages has not been documented.
+- **Long-context behavior is unverified.** The configuration declares 32,768
+  positions, but effective recall and instruction adherence at that length have not
+  been publicly measured for v8.
+- **Tool use is unverified.** Tool-format support in the tokenizer is not proof of
+  correct or safe function calling.
+- **Quantization effects.** GGUF Q4_K_M is smaller and easier to run locally, but may
+  differ from the FP16 checkpoint in accuracy, formatting, and consistency.
+- **No inherent data or action controls.** Authentication, authorization, redaction,
+  retrieval permissions, audit logging, and action approval must be provided by the
+  surrounding application.
+
+## Benchmark results
+
+No verified, reproducible benchmark scores for Nomadic-ICDU-v8 are currently
+published. A blank or estimated score would be misleading, so this release does not
+claim one.
+
+The recommended ICDU evaluation suite is:
+
+| Evaluation | What it measures | v8 public result |
+|---|---|---|
+| Intent alignment | Completion of the task's explicit objective | Not published |
+| Principle adherence | Compliance with stated priorities and rules | Not published |
+| Application / groundedness | Correct use of supplied inputs and context | Not published |
+| Constraint adherence | Compliance with required and prohibited behavior | Not published |
+| Escalation accuracy | Correct abstention or escalation when a boundary is reached | Not published |
+| Perturbation stability | Consistency under paraphrase, tone, role, and context changes | Not published |
+| Preference win rate | Blind comparison against the base model and earlier versions | Not published |
+| Human acceptance rate | Qualified reviewer acceptance on target workflows | Not published |
+| Safety regression rate | Rate of newly introduced prohibited behavior | Not published |
+| Latency and throughput | Runtime performance by engine, hardware, and context length | Not published |
+
+For a credible release result, publish the evaluation dataset or a representative,
+non-sensitive sample; scoring rubric; judge prompts; human-review procedure; sample
+sizes; confidence intervals; base-model comparison; inference settings; and exact
+checkpoint hash.
+
+## Recommended generation settings
+
+Start with deterministic decoding for governed or repeatable workflows:
+
+| Setting | Governed/default | Exploratory drafting |
+|---|---:|---:|
+| `do_sample` | `false` | `true` |
+| `temperature` | Omit when sampling is disabled | `0.4`–`0.7` |
+| `top_p` | Omit when sampling is disabled | `0.90`–`0.95` |
+| `max_new_tokens` | `256`–`512` | `512`–`1,024` |
+| `repetition_penalty` | `1.05` | `1.05` |
+| Stop token | `</s>` / tokenizer EOS | `</s>` / tokenizer EOS |
+
+Example:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Gadsdencode/Nomadic-ICDU-v8"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+messages = [
+    {"role": "system", "content": "Follow the supplied ICDU and its escalation rules."},
+    {"role": "user", "content": "Review this input and return the required action."},
+]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to(model.device)
+
+outputs = model.generate(
+    inputs,
+    do_sample=False,
+    max_new_tokens=512,
+    repetition_penalty=1.05,
+    eos_token_id=tokenizer.eos_token_id,
+)
+
+new_tokens = outputs[0, inputs.shape[-1]:]
+print(tokenizer.decode(new_tokens, skip_special_tokens=True))
+```
+
+Generation settings should be selected against the target evaluation suite. Lower
+temperature improves repeatability but does not guarantee correctness or policy
+compliance.
+
+## Context-length guidance
+
+The model configuration declares a 32,768-token maximum position length. That is an
+architectural limit, not a promise that all 32K-token prompts will be recalled or
+followed equally well.
+
+| Use case | Recommended starting context |
+|---|---:|
+| Local Q4 testing | 8,192 tokens |
+| Typical hosted workflow | 8,192–16,384 tokens |
+| Long-document evaluation | 16,384–32,768 tokens |
+| Production at 32K | Only after task-specific recall and adherence tests |
+
+Increasing context length raises KV-cache memory use, latency, and cost and can reduce
+concurrency. Prefer retrieving the smallest relevant context, placing the ICDU and
+critical constraints clearly, and testing required facts near the beginning, middle,
+and end of long prompts.
+
+If a local application reports an 8,192-token limit, that is usually its runtime
+configuration rather than a different model architecture. Increase the runtime
+setting only if the available RAM or VRAM and evaluation results support it.
+
+## Local use with LM Studio
+
+1. In LM Studio, search for `Gadsdencode/Nomadic-ICDU-v8`.
+2. Download `nomadic-icdu-v8-Q4_K_M.gguf` for the practical local build. Use the F16
+   GGUF only when the additional memory requirement is acceptable.
+3. Load the model and leave the prompt template on **Auto** or select the Mistral
+   Instruct template.
+4. Start with an 8,192-token context. Move to 16,384 or 32,768 only after checking
+   memory use and long-context behavior.
+5. For deterministic workflows, disable sampling or set temperature to the lowest
+   supported value.
+6. To expose a local API, start LM Studio's OpenAI-compatible server. The default is
+   commonly `http://127.0.0.1:1234/v1`.
+
+Check the model identifier returned by `GET /v1/models`, then use it in the request:
+
+```bash
+curl http://127.0.0.1:1234/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "replace-with-the-id-from-v1-models",
+    "messages": [
+      {
+        "role": "system",
+        "content": "Follow the supplied ICDU and its escalation rules."
+      },
+      {
+        "role": "user",
+        "content": "Review this input and return the required action."
+      }
+    ],
+    "temperature": 0,
+    "max_tokens": 512
+  }'
+```
+
+## Local use with Ollama
+
+Download the Q4_K_M GGUF:
+
+```bash
+hf download Gadsdencode/Nomadic-ICDU-v8 \
+  nomadic-icdu-v8-Q4_K_M.gguf \
+  --local-dir ./nomadic-icdu-v8
+```
+
+Create `nomadic-icdu-v8/Modelfile`:
+
+```text
+FROM ./nomadic-icdu-v8-Q4_K_M.gguf
+
+PARAMETER num_ctx 8192
+PARAMETER num_predict 512
+PARAMETER temperature 0
+PARAMETER top_p 0.9
+PARAMETER repeat_penalty 1.05
+PARAMETER stop "</s>"
+```
+
+Create and run the local model:
+
+```bash
+cd nomadic-icdu-v8
+ollama create nomadic-icdu-v8 -f Modelfile
+ollama run nomadic-icdu-v8
+```
+
+Ollama should read the chat-template metadata embedded in the GGUF. Confirm during
+acceptance testing that prompts render with Mistral `[INST] ... [/INST]` formatting.
+
+Ollama API example:
+
+```bash
+curl http://localhost:11434/api/chat \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "nomadic-icdu-v8",
+    "stream": false,
+    "messages": [
+      {
+        "role": "system",
+        "content": "Follow the supplied ICDU and its escalation rules."
+      },
+      {
+        "role": "user",
+        "content": "Review this input and return the required action."
+      }
+    ],
+    "options": {
+      "temperature": 0,
+      "num_ctx": 8192,
+      "num_predict": 512
+    }
+  }'
+```
+
+## Hosted API example
+
+For a hosted deployment, use the FP16 Safetensors checkpoint with an inference engine
+that honors the tokenizer chat template. An OpenAI-compatible API makes it easier to
+move between a hosted endpoint, LM Studio, and other serving systems.
+
+Set:
+
+```bash
+export ICDU_API_BASE_URL="https://your-endpoint.example.com/v1"
+export ICDU_API_KEY="replace-with-your-endpoint-token"
+```
+
+Python:
+
+```python
+import os
+from openai import OpenAI
+
+client = OpenAI(
+    base_url=os.environ["ICDU_API_BASE_URL"],
+    api_key=os.environ["ICDU_API_KEY"],
+)
+
+response = client.chat.completions.create(
+    model="Gadsdencode/Nomadic-ICDU-v8",
+    messages=[
+        {
+            "role": "system",
+            "content": "Follow the supplied ICDU and its escalation rules.",
+        },
+        {
+            "role": "user",
+            "content": "Review this input and return the required action.",
+        },
+    ],
+    temperature=0,
+    max_tokens=512,
+)
+
+print(response.choices[0].message.content)
+```
+
+Endpoint operators should add authentication, request-size limits, timeouts,
+structured logging with sensitive-data controls, rate limits, abuse monitoring, and
+human approval around consequential actions. Do not log raw prompts or completions by
+default when they may contain confidential data.
+
+## Model and version lineage
+
+```text
+mistralai/Mistral-7B-Instruct-v0.3
+└── Nomadic-ICDU-v8 merged FP16 checkpoint
+    ├── Transformers Safetensors (3 shards)
+    ├── GGUF F16
+    └── GGUF Q4_K_M
+```
+
+`v8` is the project's release label for this checkpoint. Public artifacts describing
+v1 through v7, their training changes, and their comparative evaluations are not
+included in this repository, so no undocumented lineage claims are made here.
+
+For reproducibility, downstream derivatives should record:
+
+- the exact source revision or checkpoint hash;
+- the ICDU and dataset manifest version;
+- training code and dependency revisions;
+- training and preference-tuning hyperparameters;
+- random seeds;
+- evaluation-suite version and release thresholds;
+- quantization tool, version, and parameters; and
+- any changes to the prompt template or context configuration.
+
+## Citation
+
+If you use this model, cite the model repository and the exact revision:
+
+```bibtex
+@misc{nomadic_icdu_v8,
+  title        = {Nomadic-ICDU-v8},
+  author       = {{Gadsdencode}},
+  year         = {2025},
+  howpublished = {\url{https://huggingface.co/Gadsdencode/Nomadic-ICDU-v8}},
+  note         = {Revision a328cf13db7a10a200cf616c2f35a0035a6b637c}
+}
+```
+
+## Additional information
+
+- Model repository: <https://huggingface.co/Gadsdencode/Nomadic-ICDU-v8>
+- Base model: <https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3>
+
+The bundled `handler.py` should be reviewed before production use. In particular,
+verify that it loads the correct model ID, applies the tokenizer's chat template,
+does not expose exception details to end users, and does not print sensitive prompts
+or completions.

config.json

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+{
+  "_name_or_path": "mistralai/Mistral-7B-Instruct-v0.3",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.44.2",
+  "use_cache": true,
+  "vocab_size": 32768
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.44.2"
+}

handler.py

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+import os
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+class EndpointHandler():
+    """
+    Custom handler for Hugging Face Inference Endpoints.
+    This handler will be used to load the model and tokenizer, and to handle inference requests.
+    """
+    def __init__(self, path=""):
+        """
+        Initializes the model and tokenizer. This method is called only once
+        when the endpoint is created.
+
+        Args:
+            path (str, optional): The path to the model directory.
+                                  If not provided, it defaults to the model loaded by the endpoint.
+        """
+        # Get the model ID from the environment variable set by Hugging Face Inference Endpoints
+        model_id = os.environ.get("HF_MODEL_ID", "Gadsdencode/Nomadic-ICDU-v8")
+        
+        print(f"Loading model: {model_id}...")
+
+        # Load the tokenizer from the pretrained model
+        self.tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+        # Load the model with recommended settings
+        # torch.bfloat16 is used for better performance on compatible hardware (e.g., Ampere GPUs)
+        # device_map="auto" automatically distributes the model across available GPUs
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_id,
+            torch_dtype=torch.bfloat16,
+            device_map="auto"
+        )
+
+        # Create a text generation pipeline
+        # This simplifies the process of generating text from a prompt
+        self.pipeline = pipeline(
+            "text-generation",
+            model=self.model,
+            tokenizer=self.tokenizer,
+        )
+        
+        print("Model and pipeline loaded successfully.")
+
+    def __call__(self, data: dict) -> list:
+        """
+        This method is called for every inference request.
+
+        Args:
+            data (dict): The request payload from the user. It contains the inputs and parameters.
+
+        Returns:
+            list: A list containing the generated text in a dictionary.
+        """
+        # Extract the prompt from the input data
+        prompt = data.get("inputs", "")
+        
+        # Extract generation parameters, with sensible defaults
+        # These parameters can be overridden by the user in the request
+        parameters = data.get("parameters", {})
+        max_new_tokens = parameters.get("max_new_tokens", 512)
+        temperature = parameters.get("temperature", 0.7)
+        top_p = parameters.get("top_p", 0.95)
+        do_sample = parameters.get("do_sample", True)
+
+        # Apply the specific prompt template required by the Nomadic-ICDU-v8 model
+        # This is crucial for getting high-quality responses from instruction-tuned models
+        formatted_prompt = f"<s>[INST] {prompt} [/INST]"
+
+        print(f"Generating text for prompt: '{prompt}'")
+        
+        # Use the pipeline to generate text
+        # We pass the formatted prompt and the generation parameters
+        try:
+            generated = self.pipeline(
+                formatted_prompt,
+                max_new_tokens=max_new_tokens,
+                do_sample=do_sample,
+                temperature=temperature,
+                top_p=top_p,
+                return_full_text=False, # Only return the generated part, not the prompt
+            )
+            
+            # The pipeline returns a list of dictionaries
+            # We extract the 'generated_text' from the first element
+            result = generated[0]
+
+        except Exception as e:
+            print(f"An error occurred during generation: {e}")
+            # Return an error message in the expected format
+            result = {"generated_text": f"Error: {e}"}
+            
+        print(f"Generated text: {result['generated_text']}")
+
+        # Return the result in a list, as expected by the Inference Endpoints framework
+        return [result]

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